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[Перевод] LIMO: Меньше — значит больше для рассуждающих LLM

Мизерный объем задач с ответами может разбудить спящего LLM-льва...
Мизерный объем задач с ответами может разбудить спящего LLM-льва...

"Мы представляем фундаментальное открытие, которое бросает вызов нашему пониманию того, как сложные рассуждения возникают в больших языковых моделях" - так нескромно начинается аннотация к свежей статье от 5 февраля 2025 года. Авторы приводят результаты эксперимента, в котором небольшое количество хорошо подобранных задач с ответами может "всколыхнуть и заставить работать" весь "спящий" внутри LLM объем знаний, которые она накопила, перелопатив гигатонны текста. Возможно, данная работа войдет в список обязательных к прочтению статей по теории нейросетей.

LIMO: Less is More for Reasoning

Yixin Ye (Соавтор-первый автор), Zhen Huang (Соавтор-первый автор), Yang Xiao, Ethan Chern, Shijie Xia, Pengfei Liu (автор-корреспондент)

Аннотация

Мы представляем фундаментальное открытие, которое бросает вызов нашему пониманию того, как сложные рассуждения возникают в больших языковых моделях. Общепринятое мнение предполагает, что сложные задачи рассуждений требуют громадного объема данных для обучения (часто > 100 000 примеров). Мы хотим продемонстрировать удивительный феномен: способности к сложным математическим рассуждениям могут быть эффективно вызваны с удивительно малым количеством примеров. Это открытие бросает вызов не только предположению о необходимости огромных объемов данных для обучения, но и распространенному мнению, что контролируемый файнтюнинг (SFT) в основном приводит к запоминанию, а не к обобщению.

Через разносторонние эксперименты мы показываем, как наша модель LIMO демонстрирует беспрецедентную производительность и эффективность в математических рассуждениях. Используя всего 817 тщательно отобранных обучающих образцов, LIMO достигает точности 57,1% на сложном бенчмарке AIME и 94,8% на MATH, улучшая производительность предыдущих сильных моделей на основе SFT с 6,5% до 57,1% на AIME и с 59,2% до 94,8% на MATH, используя при этом для обучения всего 1% от данных, требуемых предыдущими подходами.

Примечательно, что LIMO демонстрирует исключительное обобщение за пределами распределения данных, достигая абсолютного улучшения на 40,5% на 10 различных бенчмарках, превосходя модели, обученные на 100-кратно больших данных. Это прямо бросает вызов преобладающему мнению, что SFT по своей природе якобы приводит к запоминанию, а не к обобщению.

Синтезируя эти новаторские результаты, мы предлагаем Гипотезу Рассуждений "Меньше — значит больше" (LIMO Hypothesis): В базовых моделях, где доменные знания были всесторонне закодированы во время предварительного обучения, сложные способности к рассуждениям могут возникать через минимальные, но точно организованные демонстрации когнитивных процессов.

Эта гипотеза предполагает, что порог вызова сложных рассуждений не ограничен сложностью целевой задачи рассуждений, а определяется двумя ключевыми факторами:

  1. Полнотой закодированной базы знаний модели во время предварительного обучения.

  2. Эффективностью примеров после обучения, которые служат "когнитивными шаблонами", показывающими модели, как эффективно использовать свою существующую базу знаний для решения сложных задач рассуждений.

Для обеспечения воспроизводимости и поддержки будущих исследований в области эффективного использования данных мы выпускаем LIMO как комплексный открытый набор инструментов: https://github.com/GAIR-NLP/LIMO .

Рисунок 1: LIMO достигает существенного улучшения по сравнению с NuminaMath, используя меньше образцов, при этом превосходя на разнообразных математических и междисциплинарных бенчмарках. Слева: Полностью та же основа, 1% данных = 778% выигрыша в AIME 24 (pass@1). Справа: превосходная производительность во всех 10 бенчмарках.
Рисунок 1: LIMO достигает существенного улучшения по сравнению с NuminaMath, используя меньше образцов, при этом превосходя на разнообразных математических и междисциплинарных бенчмарках. Слева: Полностью та же основа, 1% данных = 778% выигрыша в AIME 24 (pass@1). Справа: превосходная производительность во всех 10 бенчмарках.

1. Введение

Сложные рассуждения долгое время считались одной из самых непростых способностей для привития большим языковым моделям (LLM). Хотя недавние работы показали, что LLM могут быть эффективно адаптированы к предпочтениям пользователей с помощью относительно небольшого количества инструкций (Zhou et al., 2024a), обучение моделей рассуждать — особенно в математике и программировании — широко считается требующим значительно большего количества обучающих примеров (Paster et al., 2023; Yue et al., 2024). Это общепринятое мнение проистекает из присущей задачам рассуждений сложности, что требует многошаговой логической дедукции, применения базовых знаний и структурированных путей решения. Результирующая парадигма обычно включает обучение на десятках или сотнях тысяч примеров (Yu et al., 2024; Li et al., 2024b), основываясь на двух фундаментальных предположениях: во-первых, что освоение таких сложных когнитивных процессов требует большого количества контролируемых выводов модели, и во-вторых, что контролируемый файнтюнинг (SFT) приводит в основном к запоминанию, а не к истинному обобщению (Zhang et al., 2024; Xu et al., 2024; Chu et al., 2025).

Хотя этот подход показал успех, он налагает на исследователя значительные вычислительные затраты и бремя сбора большого объема данных.

Мы утверждаем, что существующая парадигма, требующая больших объемов данных, больше не актуальна. Недавние достижения фундаментально изменили понимание того, как LLM в приобретают, организуют и используют знания для рассуждений, что предполагает возможность более эффективного подхода. В частности, два ключевых направления развития, создали условия для фундаментального переосмысления того, как мы подходим к рассуждениям в LLM:

  1. Революция в фундаментальных знаниях: Современные базовые модели теперь включают беспрецедентное количество математического контента во время предварительного обучения (Qwen et al., 2025; Yang et al., 2024; Wang et al., 2024). Например, суммарный объем данных для обучения Llama 2 во всех доменах составил 1,8 трлн токенов (Touvron et al., 2023), в то время как Llama 3 использовала 3,7 трлн токенов только для математических рассуждений (Grattafiori et al., 2024). Это говорит о том, что современные LLM уже могут обладать богатыми математическими знаниями в своем параметрическом пространстве, таким образом, задача приобретения знаний преобразуется в задачу по их извлечению.

  2. Революция в масштабировании вычислений во время вывода: Появление техник, масштабирующих длинные цепочки рассуждений, показало, что эффективные рассуждения требуют значительного вычислительного пространства во время вывода. Недавние работы (OpenAI et al., 2024; Qin et al., 2024; Huang et al., 2024) показали, что предоставление моделям возможности генерировать расширенные цепочки рассуждений значительно улучшает их способность к рассуждениям. По сути, вычисления во время вывода предоставляют критически важное когнитивное пространство, где модели могут систематически распаковывать и применять свои знания, полученные в результате предварительного обучения.

Мы предполагаем, что успешные рассуждения возникают благодаря синергии этих двух факторов: богатых знаний, полученных в предварительном обучении и достаточных вычислительных ресурсов во время вывода. Эти разработки в совокупности дают поразительную возможность:

если модели обладают богатыми знаниями для рассуждений и им предоставлено достаточное вычислительное пространство, то активация их способностей к рассуждениям может потребовать лишь небольшого количества высококачественных обучающих образцов, которые поощряют расширенное обдумывание, а не массивные наборы данных для тонкой настройки.

Опираясь на это понимание, мы предлагаем Гипотезу Рассуждений "Меньше — значит больше" (LIMO Hypothesis). Эта гипотеза определяет два критических фактора, которые определяют порог извлечения сложных рассуждений: (1) латентное наличие предварительных знаний в параметрическом пространстве модели и (2) эффективность минимальных примеров в демонстрации систематических процессов решения проблем, которые поощряют расширенное обдумывание. Критически важное предположение состоит в том, что эффективность выборок для извлечения продвинутых рассуждений не ограничена сложностью целевой задачи рассуждений, а скорее полнотой закодированной базы знаний модели и ее воздействием на обучающие образцы, которые эффективно используют вычислительное пространство во время вывода.

Через всесторонние эксперименты мы демонстрируем, что LIMO достигает точности 57,1% на сложном бенчмарке AIME и 94,8% на MATH, используя всего 817 обучающих образцов, превосходя предыдущие сильные модели на основе SFT, используя всего 1% их данных для обучения. Наиболее примечательно, что эти преимущества обобщаются на разнообразный спектр невиданных ранее сценариев, причем LIMO последовательно превосходит модели, обученные на 100x больше данных, с абсолютным улучшением на 40,5%.

Это открытие имеет глубокие последствия для исследований в области искусственного интеллекта: оно предполагает, что даже способности к сложным рассуждениям (на уровне проводимых соревнований LLM) могут быть эффективно вызваны с помощью минимальных, но тщательно отобранных обучающих образцов.

Фундаментально это указывает на многообещающий технический путь к AGI — любая сложная способность к рассуждениям, независимо от ее сложности, потенциально может быть активирована с минимальными образцами при двух ключевых условиях: (1) достаточные доменные/базовые знания, встроенные во время предварительного обучения, и (2) оптимальные когнитивные цепочки рассуждений для активации.

Это представляет собой не просто аргумент в пользу эффективности данных, но фундаментальное понимание того, как сложные способности к рассуждениям возникают в больших языковых моделях.

Основные достижения этой работы:

  1. Мы устанавливаем гипотезу LIMO, демонстрируя, что сложные способности к рассуждениям могут быть вызваны с помощью неожиданно небольших наборов данных (сотни примеров), используя богатые математические знания в предварительно обученных моделях и детализированные цепочки рассуждений.

  2. Мы предоставляем эмпирические доказательства, бросающие вызов текущим предположениям о законах масштабирования в задачах рассуждений, показывая, что преимущества устойчиво обобщаются на проблемы за пределами распределения данных, что указывает на приобретение подлинных способностей к рассуждениям, а не поверхностного сопоставления шаблонов.

  3. Мы определяем критические факторы для эффективного извлечения рассуждений, особенно синергию между предварительно обученными базами знаний и масштабированием вычислений во время тестирования, предоставляя понимание того, как эти достижения могут быть объединены для достижения превосходной производительности в рассуждениях с минимальными данными для файнтюнинга.

  4. Мы выкладываем в открытый доступ комплексный набор инструментов, включая наши тонко настроенные модели, пайплайны для оценки, код обучения и тщательно отобранные наборы данных с различными уровнями качества. Это позволяет систематически исследовать эффективность данных в сложных рассуждениях и способствует воспроизводимости наших результатов, предоставляя ценные ресурсы для будущих исследований в этом направлении.

Таблица 1: Сравнительный анализ: Феномен "Меньше — значит больше" в языковых моделях

Аспект

Общее согласование вывода (LIMA)

Сложные рассуждения (LIMO)

Основная способность

Адаптация формата и стиля ответов для общего взаимодействия

Многошаговый логический вывод и сложные когнитивные рассуждения

Фундамент знаний

• Достаточно общего текстового корпуса

• Разнообразные парадигмы рассуждений и подходы к решению проблем

• Паттерны социального взаимодействия

• Богатый контекст для исследования альтернативных решений

• Базовые знания о мире

• Глубокие концептуальные связи между доменами знаний

Требования к вычислениям

• Достаточно генерации фиксированной длины

• Масштабируемые вычисления во время вывода

• Достаточно однопроходной обработки

• Требуется поддержка расширенных цепочек рассуждений

• Приемлемо ограниченное окно контекста

• Необходимо большое когнитивное пространство

Исторические предпосылки

Появилось в 2023 году, требовало только:

Появилось в 2025 году, потребовало конвергенции:

• Базовые модели с общими знаниями

• Продвинутые архитектуры для рассуждений

• Базовые техники инженерии промптов

• Революция в масштабировании вычислений во время вывода

Качество обучающих данных

Дизайн вопросов:

Дизайн вопросов:

– Типичные сценарии взаимодействия

– Высокосложные задачи, стимулирующие сложные рассуждения

– Стандартное разнообразие задач

– Задачи, отклоняющиеся от распределения обучающих данных

– Базовое следование инструкциям

– Задачи, требующие интеграции знаний из разных доменов

Качество решений:

Качество решений:

– Четкий стиль коммуникации

– Оптимальная структура с адаптивным разбиением шагов

– Согласованность формата

– Стратегическое когнитивное структурирование для рассуждений

– Подходящий тон

– Тщательная проверка на протяжении всего решения

2. Переосмысление феномена: "Меньше — значит больше" и масштабирование RL (обучения с подкреплением)

Появление LIMO представляет собой смену парадигмы в том, как мы концептуализируем и активируем в LLM сложные способности к рассуждениям. В этом разделе рассматриваются два ключевых сравнения, которые проливают свет на фундаментальную природу этого прогресса: во-первых, сравнение LIMO с LIMA для понимания того, как принципы "Меньше — значит больше" распространяются от общего согласования вывода модели до сложных рассуждений; и во-вторых, сравнение LIMO с подходами масштабирования на основе обучения с подкреплением (RL), чтобы выделить различные философские перспективы развития способностей к рассуждениям. Используя этот анализ, мы стремимся установить более глубокое понимание того, как сложные когнитивные способности возникают в языковых моделях и какие условия позволяют реализовать их эффективную активацию.

2.1. LIMO vs LIMA

Появление феномена "Меньше — значит больше" в LLM представляет собой фундаментальный сдвиг в нашем понимании того, как сложные способности модели могут быть реализованы с минимальными данными. В то время как LIMA (Zhou et al., 2024a) впервые продемонстрировала этот феномен в контексте общего согласования вывода модели, распространение этого принципа на сложные математические рассуждения представляет уникальные требования и вызовы. В этом разделе исследуются ключевые разработки, которые делают возможным принцип "Меньше — значит больше" для рассуждений, анализируются существенные различия между сценариями согласования вывода модели и рассуждений модели, а также предоставляются инсайты об условиях, необходимых для эффективной активации способностей в больших языковых моделях.

Революция в фундаменте знаний
В течение последних двух лет мы стали свидетелями трансформации понимания того, как языковые модели приобретают и организуют математические знания. В то время как LIMA могла полагаться на общие текстовые корпуса для согласования вывода, успех LIMO основывается на богатом математическом контенте, который теперь встроен в современные базовые модели через специализированное предварительное обучение (Wang et al., 2024). Этот специализированный фундамент знаний служит предпосылкой для эффективной активации способностей к рассуждениям.

Революция в вычислительных возможностях
Ключевое различие между LIMA и LIMO заключается в их вычислительных требованиях. В то время как задачи согласования вывода LIMA могли быть выполнены с помощью генерации текста фиксированной длины и однопроходной обработки, задачи рассуждений LIMO требуют обширного вычислительного пространства для многошагового обдумывания. Появление техник масштабирования вычислений во время вывода (OpenAI et al., 2024; Qin et al., 2024) предоставило необходимое "когнитивное пространство", где модели могут систематически распаковывать и применять свои знания, полученные при предварительном обучении.

Синергетическая конвергенция
Время открытия LIMO отражает конвергенцию этих двух революций. Двухлетний разрыв между LIMA и LIMO представляет собой не просто время, необходимое для улучшения предварительно обученных моделей, но и необходимое ожидание прорывов в вычислениях во время вывода. Эта конвергенция делает возможным феномен, который мы называем Порогом извлечения рассуждений: когда модели обладают как богатыми доменными знаниями, так и достаточным вычислительным пространством, сложные способности к рассуждениям могут быть активированы с помощью минимальных, но точных демонстраций таких рассуждений.

Последствия для будущих исследований
Этот сравнительный анализ раскрывает идею "Меньше — значит больше" не просто как аргумент в пользу использования меньшего количества данных, но как фундаментальный принцип, управляющий эффективным извлечением способностей моделей. Успех LIMO демонстрирует, что когда основные предпосылки (фундамент знаний и вычислительная структура) выполнены, сложные способности могут быть вызваны с замечательной эффективностью. Этот инсайт предполагает новое направление исследований: систематическое определение предпосылок и оптимальных условий активации для различных способностей.

Далее необходимо исследовать, возможно ли достичь аналогичной эффективности в реализации других продвинутых способностей (например, планирование, креативное решение проблем), как только будут установлены соответствующие им знания и вычислительные основы. Таким образом, принцип "Меньше — значит больше" служит как теоретической основой для понимания возникновения способностей, так и практическим руководством для достижения эффективного развития способностей в различных областях.

Таблица 2: Сравнительный анализ подходов LIMO и масштабирования RL

Аспект

Масштабирование RL (например, o1, R1)

LIMO

Основной принцип

Реализация общего принципа: поиск оптимальных траекторий рассуждений через RL

Фундаментальный принцип: способности к рассуждениям существуют и нуждаются в активации через высококачественные траектории рассуждений

Природа решения

Обнаружение траекторий рассуждений через обширное исследование на основе RL

Прямое построение высококачественных траекторий рассуждений на основе когнитивного понимания

Основной вызов

Как эффективно искать эффективные траектории рассуждений в большом пространстве решений

Как определить и построить оптимальные траектории рассуждений, которые активируют существующие способности

Методология

Неявное обнаружение траекторий через крупномасштабную оптимизацию RL

Явное проектирование траекторий через когнитивные шаблоны

Стратегия поиска

Широкое исследование пространства решений с использованием вычислительных ресурсов

Целевое исследование, направляемое когнитивными принципами

Эффективность использования ресурсов

Ресурсоемкий процесс поиска

Ресурсоэффективное прямое построение

Обобщение

Через обширное сэмплирование пространства траекторий

Через понимание фундаментальных паттернов рассуждений

2.2. LIMO vs Масштабирование RL

Появление двух различных подходов к развитию способностей к рассуждениям в больших языковых моделях (масштабирование RL и LIMO) представляет фундаментальное расхождение в том, как мы понимаем и улучшаем интеллект моделей. Масштабирование RL, примером которого являются работы, такие как o1 (OpenAI, 2024) и DeepSeek-R1 (Guo et al., 2025), подходит к задаче с точки зрения инженерной оптимизации. Оно предполагает, что способности к рассуждениям необходимо интенсивно обучать в моделях через крупномасштабное обучение с подкреплением (RL). Хотя этот подход эффективен, он по сути рассматривает RL как механизм широкого поиска для обнаружения эффективных паттернов рассуждений с использованием огромных вычислительных ресурсов.

В отличие от этого LIMO предлагает более фундаментальную перспективу: способности к рассуждениям уже скрыты в предварительно обученных моделях, они заложены уже на этапе предварительного обучения.

Ключевой вызов смещается с "обучения" на "извлечение" — поиск точных когнитивных шаблонов, которые могут активировать эти врожденные способности.

С этой точки зрения, подходы масштабирования RL, такие как DeepSeek-R1, можно рассматривать как конкретные реализации этого принципа, использующие обучение с подкреплением как механизм поиска таких траекторий. Хотя оба подхода в конечном итоге стремятся к высококачественным решениям для рассуждений, LIMO предлагает более принципиальный и прямой путь через явное проектирование траекторий, в то время как масштабирование RL обнаруживает эти траектории через обширное вычислительное исследование. Это изменение подхода предполагает, что различные методы, включая RL, экспертный дизайн или гибридные подходы, могут быть поняты и оценены в рамках концепции LIMO как различные стратегии для поиска оптимальных траекторий рассуждений.

3. Датасет LIMO

3.1. Гипотеза LIMO

Мы формализуем Гипотезу Рассуждений "Меньше — значит больше" (LIMO Hypothesis) следующим образом:

В базовых моделях, где доменные знания были всесторонне закодированы во время предварительного обучения, сложные способности к рассуждениям могут возникать через минимальные, но точно организованные демонстрации когнитивных процессов.

Эта гипотеза основывается на двух фундаментальных предпосылках:

  1. Латентное наличие предварительных знаний в параметрическом пространстве модели.

  2. Качество цепочек рассуждений, которые точно разбивают сложные проблемы на детализированные, логические шаги, делая когнитивный процесс явным и отслеживаемым.

Для проверки этой гипотезы мы предлагаем систематический подход к созданию высококачественного, минимального набора данных, который может эффективно активировать врожденные способности модели к рассуждениям.

3.2 Определение задачи

В этой статье мы сосредоточимся на задаче рассуждений с объективно проверяемыми ответами. Для заданного вопроса q \in Q , где Q представляет пространство задач для рассуждения, цель состоит в том, чтобы сгенерировать ответ a \in A и цепочку рассуждений r \in R .

Мы определяем цепочку рассуждений r как последовательность промежуточных шагов {s_1, s_2, ..., s_n} , где каждый шаг s_i представляет собой логический вывод, который связывает вопрос с конечным ответом.

Формально процесс рассуждений можно представить как функцию f :

f : Q \rightarrow R \times A \quad (1)

Таким образом, качество результирующего набора данных D определяется двумя фундаментальными, но многогранными компонентами:

  1. Качество вопросов
    q \in Q , которое включает такие факторы, как разнообразие подходов к решению задач, соответствующий уровень сложности для проверки способностей модели и широта охватываемых областей знаний.

  2. Качество решений
    (r, a) \in R \times A , которое включает такие аспекты, как обучающая ценность, логическая согласованность и методологическая строгость.

Вопросы должны быть разработаны так, чтобы поощрять сложные паттерны рассуждений и интеграцию знаний, а решения должны демонстрировать четкую логическую цепочку и служить эффективными примерами для обучения. Эти взаимосвязанные аспекты качества, среди прочего, направляют наш систематический процесс курирования (оценки, фильтрации) данных, подробно описанный в следующих разделах.

3.3. Создание высококачественного набора данных

Наш процесс курирования данных сосредоточен на создании высококачественного набора данных D = {(q_i, r_i, a_i)}_{i=1}^N, где N намеренно остается небольшим, чтобы подтвердить нашу гипотезу LIMO.

3.3.1. Выбор вопросов

Мы предполагаем, что качественные вопросы q \in Q должны естественным образом вызывать расширенные процессы рассуждений. Наши критерии выбора следующие:

  • Уровень сложности: Мы отдаем приоритет сложным задачам, которые стимулируют создание сложных цепочек рассуждений, разнообразных мыслительных процессов и интеграцию знаний, позволяя LLM эффективно использовать предварительно обученные знания для качественного вывода.

  • Общность: Задачи, которые отклоняются от распределения обучающих данных модели, могут лучше проверять ее фиксированные шаблоны мышления, поощряя исследование новых подходов к рассуждениям и расширяя пространство поиска выводов.

  • Разнообразие знаний: Выбранные задачи должны охватывать различные математические области и концепции, требуя от модели интеграции и связи удаленных знаний в процессе решения задач.

Для эффективной реализации этих критериев мы сначала собрали обширный пул кандидатов из различных авторитетных наборов данных:

  • NuminaMath-CoT, содержащий тщательно аннотированные задачи от уровня средней школы до продвинутых соревнований.

  • Задачи исторических экзаменов AIME, известные своей чрезвычайной сложностью и интеграцией задач из различных математических областей.

  • MATH (Hendrycks et al., 2021), включающий задачи из престижных математических соревнований.

  • Другие источники математических задач.

К этому богатому начальному набору мы применили системный многоэтапный процесс фильтрации. Начав с выборки из десятков миллионов задач, мы сначала применили базовый фильтр сложности с помощью модели Qwen2.5-Math-7B-Instruct (Yang et al., 2024), исключая задачи, которые эта модель могла решить правильно за несколько попыток. Это помогло установить предварительный порог сложности.

Затем оставшиеся задачи были подвергнуты более строгой оценке с использованием современных моделей рассуждений, включая R1, DeepSeek-R1-Distill-Qwen-32B (Guo et al., 2025) и модели из работы Huang et al. (2024), оставляя только те задачи, где даже эти модели показывали успешность ниже определенного порога после нескольких итераций. Наконец, для поддержания разнообразия мы использовали стратегические методы выборки, балансируя степень представленности различных математических областей и уровней сложности, но избегая концептуальной избыточности.

Этот тщательный процесс отбора в итоге дал 817 тщательно отобранных задач из начального пула десятков миллионов кандидатов. Выбранные задачи соответствуют нашим строгим критериям качества и охватывают широкий спектр математических вызовов.

3.3.2. Построение цепочек рассуждений

Помимо качественных вопросов/задач, качество приведенных решений этих задач играет ключевую роль в обучении. Для курирования качественных решений мы приняли комплексную стратегию отбора. Мы начали с сбора официальных решений для задач, где они были доступны, дополненных решениями, написанными экспертами-людьми и специалистами по ИИ. Кроме того, мы использовали современные модели рассуждений, такие как DeepSeek R1, DeepSeek-R1-Distill-Qwen-32B (Guo et al., 2025) и Qwen2.5-32b-Instruct, для генерации разнообразных подходов к решению.

Следуя методологии, предложенной в O1-Journey-Part2 (Huang et al., 2024), мы также использовали техники самообучения на основе Qwen2.5-32b-Instruct для создания дополнительных вариантов моделей, которые затем использовались для генерации дополнительных ответов на задачи. Эти ответы были отфильтрованы по правильности ответов, чтобы создать базовую коллекцию валидных решений.

Затем авторы провели всесторонний анализ этих отфильтрованных решений через совместное изучение. В результате мы выделили несколько ключевых характеристик, которые отличают высококачественные цепочки рассуждений:

  1. Оптимальная структурная организация: Решение демонстрирует четкую и хорошо организованную структуру с адаптивной последовательностью шагов. Особенно важно, что оно выделяет больше токенов и деталей на ключевых этапах рассуждений, сохраняя краткость для простых шагов.

  2. Эффективное когнитивное структурирование: Высококачественные решения обеспечивают стратегическую образовательную поддержку, постепенно формируя понимание через тщательно структурированные объяснения.

  3. Тщательная проверка: Высококачественные решения включают частые шаги проверки на протяжении всего процесса рассуждений, такие как проверка промежуточных результатов и подтверждение логической согласованности.

На основе этих характеристик мы разработали гибридный подход, сочетающий правило-ориентированную фильтрацию и курирование с помощью LLM, чтобы выбрать высококачественные решения для каждой задачи. Этот систематический процесс гарантирует, что каждое выбранное решение соответствует нашим критериям качества, сохраняя согласованность в наборе данных.

Результирующий набор данных D состоит из тщательно отобранных троек (q, r, a) , где каждая цепочка рассуждений r удовлетворяет нашим критериям качества. Сохраняя эти строгие стандарты при ограниченном размере датасета |D| , мы стремимся продемонстрировать, что высококачественные демонстрации решений/размышлений, а не большой объем данных, являются ключом к активации сложных способностей к рассуждениям.

4. Методология

Основываясь на принципе "Меньше — значит больше", модель с обширными знаниями для рассуждений, полученными в ходе предварительного обучения, и способностью выполнять длинные цепочки рассуждений во время тестирования, может развить устойчивые способности к рассуждениям. После обучения на всего нескольких сотнях примеров данных для контролируемого файнтюнинга (SFT), модель учится интегрировать мета-рассуждения в связную цепочку рассуждений.

4.1. Протокол обучения

Мы проводим тонкую настройку модели Qwen2.5-32B-Instruct с использованием контролируемой тонкой настройки на нашем наборе данных LIMO. Процесс обучения включает полную тонкую настройку параметров с оптимизацией DeepSpeed ZeRO-3 (Rajbhandari et al., 2020) и FlashAttention-2 (Dao, 2023), с ограничением длины последовательности в 16 384 токена.

4.2. Фреймворк оценки

Оценка в рамках домена
Для всесторонней оценки производительности моделей в различных задачах рассуждений мы разработали фреймворк оценки, включающий как традиционные, так и новые бенчмарки. Наш основной набор для оценки включает несколько известных математических соревнований и бенчмарков:

  • American Invitational Mathematics Examination (AIME24)

  • MATH500 (Hendrycks et al., 2021)

  • American Mathematics Competitions (AMC23)

Оценка за пределами распределения данных (Out-of-Distribution, OOD)
Для тщательной оценки производительности моделей на задачах, выходящих за пределы распределения обучающих данных, мы тщательно отобрали бенчмарки, которые отличаются от наших обучающих данных по различным аспектам. Эти бенчмарки можно разделить на три группы:

  1. Разнообразные математические соревнования: Мы выбрали OlympiadBench (He et al., 2024), который представляет собой отдельное распределение математических задач для проверки OOD-производительности моделей.

  2. Новые многоязычные бенчмарки: Чтобы минимизировать загрязнение данных, мы создали несколько бенчмарков, используя задачи из самых последних экзаменов:

    - CHMath из Китайского математического соревнования для старшеклассников 2024 года,

    - Gaokao из Национального вступительного экзамена Китая 2024 года,

    - Kaoyan из вступительных экзаменов в аспирантуру Китая,

    - GradeSchool, наш новый бенчмарк для задач по математике начальной школы.

    Все задачи в этих бенчмарках написаны на китайском языке, в то время как наши обучающие данные не содержат задач на китайском. Это добавляет дополнительное измерение OOD, оценивая не только способность модели обобщать задачи из разных распределений, но и ее способность к рассуждениям на незнакомых языках.

  3. Многодисциплинарные бенчмарки: Для оценки более широких возможностей обобщения за пределами математики (нашей обучающей области) мы включили:

    - Minerva (Lewkowycz et al., 2022), который включает задачи уровня бакалавриата по STEM.

    - GPQA (Rein et al., 2023), который оценивает способности к рассуждениям в различных дисциплинах и когнитивных уровнях.

Метрики производительности

Мы оцениваем производительность с использованием метрики pass@1 на всех наших бенчмарках. Все оценки проводятся в условиях Zero-shot Chain-of-Thought (CoT), чтобы лучше оценить способности модели к рассуждениям. Для бенчмарков, включая MATH500, OlympiadBench, Gaokao, Kaoyan, GradeSchool, MinervaMath и GPQA, мы используем простой подход с жадным декодированием и одним образцом для оценки правильности.

Однако для небольших бенчмарков, содержащих менее 50 задач каждый (в частности, AIME24, AMC23 и CHMATH), мы применяем более тщательный протокол оценки, генерируя 16 образцов с температурой 0,7 и рассчитывая несмещенную метрику pass@1, как предложено в Chen et al. (2021).

Для задач, где ответы представляют собой структурированные числовые значения, мы применяем правило-ориентированную оценку для проверки математической эквивалентности. Для более сложных форматов ответов (например, выражений, уравнений или структурированных решений) мы используем оценщик на основе LLM, который был проверен на высокую надежность.

Во всех оценках мы поддерживаем максимальную длину вывода в 32 768 токенов, чтобы минимизировать вероятность усечения вывода и обеспечить полную оценку попыток решения задач. Кроме того, при оценке LIMO мы заметили, что масштабирование во время вывода иногда приводит к повторяющимся паттернам в конце длинных выводов. В таких случаях мы извлекаем наиболее вероятный окончательный ответ из ответа модели для обеспечения точной оценки ее способностей к решению задач.

5. Эксперимент

5.1. Базовые модели

Мы сравниваем LIMO с набором базовых моделей, включая следующие известные модели:

  • OpenAI-o1-preview (OpenAI, 2024): крупная языковая модель, демонстрирующая продвинутые способности к математическим рассуждениям в различных сложных задачах.

  • QwQ-32B-Preview (Team, 2024b): модель, специально разработанная для решения математических задач с сильными способностями к рассуждениям.

  • Qwen2.5-32B-Instruct: наша базовая модель для сравнительного анализа.

Для оценки мы используем API OpenAI для доступа к OpenAI-o1-preview, а для развертывания других моделей с открытыми весами (например, QwQ-32B-Preview) используем VLLM (Kwon et al., 2023). Для обеспечения честного сравнения все модели следуют одинаковому протоколу оценки с идентичными гиперпараметрами вывода.

Чтобы исследовать влияние эффективности данных для обучения, мы проводим сравнительные эксперименты, используя популярные открытые наборы данных для рассуждений для контролируемой тонкой настройки на нашей базовой модели. Для честного сравнения все эксперименты используют ту же архитектуру LLM, что и LIMO, чтобы различия в производительности были обусловлены исключительно характеристиками обучающих данных.

5.2. Основные результаты

Мы сравниваем LIMO с двумя крупными наборами данных:

  1. OpenThoughts-114k: синтетический набор данных для рассуждений, содержащий 114 тыс. примеров, охватывающих математику, науку, программирование и головоломки. Решения следуют структурированному формату рассуждений, сгенерированному DeepSeek-R1.

  2. NuminaMath-100k: случайно выбранное подмножество из 100 тыс. примеров из NuminaMath-CoT, включающее математические задачи от китайских школьных упражнений до международных олимпиад. Каждое решение следует формату Chain of Thought (CoT) (Wei et al., 2022).

Эти наборы данных содержат значительно больше примеров, чем обучающий набор LIMO (817 примеров), что позволяет нам изучить взаимосвязь между количеством данных и производительностью модели.

Основные результаты

Наши экспериментальные результаты демонстрируют превосходство LIMO как в рамках домена, так и за его пределами, как показано в Таблице 3.

Таблица 3: Сравнение производительности моделей (pass@1) на различных математических бенчмарках
Модели включают современные LLM (OpenAI-o1-preview, QwQ-32B-Preview), нашу базовую модель (Qwen2.5-32B-Instruct) и модели, тонко настроенные на различных наборах данных. Размеры обучающих данных указаны в скобках. Лучшие результаты для каждого бенчмарка выделены жирным шрифтом. Наша модель LIMO (выделена синим) демонстрирует превосходную производительность, несмотря на использование значительно меньшего количества обучающих примеров (817) по сравнению с другими тонко настроенными моделями (более 100 тыс.).

Набор данных

OpenAI-o1-preview

Qwen2.5-32B-Instruct

QwQ-32B-preview

OpenThoughts (114k)

NuminaMath (100k)

LIMO (ours, 817)

В рамках домена

AIME24

44.6

16.5

50.0

50.2

6.5

57.1

MATH500

85.5

79.4

89.8

80.6

59.2

94.8

AMC23

81.8

64.0

83.6

80.5

40.6

92.0

За пределами домена

OlympiadBench

52.1

45.3

58.5

56.3

36.7

66.8

CHMath

50.0

27.3

68.5

74.1

11.2

75.4

Gaokao

62.1

72.1

80.1

63.2

49.4

81.8

Kaoyan

51.5

48.2

70.3

54.7

32.7

73.4

GradeSchool

62.8

56.7

63.8

39.0

36.2

76.2

Minerva

47.1

41.2

39.0

41.1

24.6

44.9

GPQA

73.3

48.0

65.1

42.9

25.8

66.7

Среднее

61.1

49.9

66.9

58.3

32.3

72.8

Производительность в рамках домена
На задачах в рамках домена LIMO показывает лучшие результаты на всех бенчмарках. На AIME24 LIMO достигает точности 57,1%, значительно опережая QwQ-32B-Preview (50,0%) и OpenAI-o1-preview (44,6%). На MATH500 LIMO достигает точности 94,8%, превосходя QwQ-32B-Preview (89,8%) и OpenAI-o1-preview (85,5%). На AMC23 LIMO достигает точности 92,0% по сравнению с 83,6% у QwQ-32B-Preview.

Обобщение за пределами домена (Out-of-Distribution, OOD)
LIMO демонстрирует сильные способности к обобщению на разнообразных задачах за пределами домена. На OlympiadBench LIMO достигает точности 66,8%, значительно опережая QwQ-32B-Preview (58,5%) и базовую модель (45,3%). Аналогичные улучшения наблюдаются на других сложных бенчмарках, таких как CHMath (75,4% против 68,5%) и GradeSchool (76,2% против 63,8%). На GPQA LIMO показывает конкурентоспособный результат 66,7%, близкий к результату лидера OpenAI-o1-preview (73,3%).

Сравнение с крупными наборами данных
Наши эксперименты показывают, что, несмотря на больший объем данных, оба базовых набора данных показывают худшие результаты по сравнению с LIMO. NuminaMath-100k демонстрирует значительное снижение производительности (32,3% против 49,9% у базовой модели) из-за плохого отбора цепочек рассуждений, а OpenThoughts-114k показывает результаты 58,3%, вероятно, из-за нецелевого выбора задач. В то же время тщательно отобранные 817 задач LIMO обеспечивают превосходную производительность (72,8%), демонстрируя, что целевой отбор и высококачественные аннотации более важны, чем объем данных для развития устойчивых способностей к рассуждениям.

Общая производительность
LIMO достигает наивысшей средней производительности 72,1% на всех бенчмарках, значительно опережая OpenAI-o1-preview (67,8%), QwQ-32B-Preview (66,4%) и другие базовые модели. Это всесторонняя оценка демонстрирует, что тщательно отобранный подход к обучению LIMO с использованием всего 817 примеров может превзойти модели, обученные на наборах данных, которые на порядки больше.

5.3. Анализ

5.3.1. Исследование 1: Влияние качества цепочек рассуждений

Чтобы глубже понять, почему подход "Меньше — значит больше" дает такие впечатляющие результаты, мы исследуем качество цепочек рассуждений (CoT). Возникает фундаментальный вопрос: какие характеристики определяют высококачественную цепочку рассуждений, которая приводит к превосходной производительности модели? Для этого мы провели контролируемое сравнительное исследование, изучая, как решения разного качества для одних и тех же задач влияют на производительность моделей, обученных на них.

Настройка эксперимента
Для этого анализа мы выбрали 500 задач из набора данных LIMO. Выбор был основан на пересечении задач, для которых модели, используемые в отбраковке, показали различия в производительности, и задач с соответствующими аннотированными человеком решениями, чтобы обеспечить согласованность сравнений. Для этих 500 задач мы собрали и классифицировали решения по пяти уровням качества на основе нашей комплексной системы оценки. Эти решения были получены из различных источников, включая экспертов-людей, специалистов по ИИ, а также ответы, сгенерированные моделями. Решения были классифицированы строго на основе их качества рассуждений, а не их источника.

Метрика качества
Следуя принципам, изложенным в разделе 3.3.2, мы использовали комплексный подход для классификации цепочек рассуждений на пять уровней качества (L1-L5, где L5 — наивысший). Наша оценка сосредоточена на нескольких ключевых аспектах: насколько хорошо организованы и связаны шаги, правильно ли объяснены важные логические переходы и включает ли решение шаги самопроверки. Используя эти критерии, мы классифицировали решения уровня L5 как те, которые демонстрируют отличную организацию с четкими, хорошо объясненными шагами и тщательной самопроверкой. Решения уровня L4 также хорошо структурированы, но с менее строгой проверкой. Решения уровня L3 показывают достойную организацию, но иногда пропускают объяснение ключевых логических переходов. Решения L2 часто содержали сокращенные рассуждения без особых объяснений, решения L1 обычно просто перечисляли основные шаги с минимальной детализацией и редко включали какую-либо проверку.

Результаты
Результаты нашего обучения (рис. 2) сильно коррелируют с уровнем качества логических цепочек. Модели, обученные на качественных логических цепочках L5, достигли наивысшей производительности как в AIME24, так и в MATH500, продемонстрировав эффективность хорошо структурированных, подробных и самопроверенных рассуждений. Производительность последовательно снижалась с каждым уровнем качества, при этом L4 и L3 показали умеренный успех, в то время как L2 и L1 показали заметно более низкую производительность. Эти результаты эмпирически подтверждают нашу систему оценки качества и подчеркивают ключевую роль в производительности модели играют высококачественные логические цепочки. В частности, мы заметили, что разница в производительности между решениями L5 и L1 была существенной - примерно 15 процентных пунктов для AIME24 и 12 процентных пунктов для MATH500. Это существенное различие говорит о том, что качество логических цепочек играет гораздо более важную роль в работе модели, чем предполагалось ранее, что подчеркивает важность тщательного анализа обучающих данных для включения в них хорошо структурированных решений.

Рисунок 2, Сравнение моделей, обученных на логических цепочках разного уровня качества.
Рисунок 2, Сравнение моделей, обученных на логических цепочках разного уровня качества.

5.3.2 Исследование 2: Влияние качества вопросов

Мы предположили, что более сложные задачи способствуют созданию сложных цепочек рассуждений, разнообразных мыслительных процессов и улучшенной интеграции знаний, что позволяет LLM лучше использовать предварительно обученные знания для качественного вывода. Чтобы проверить эту гипотезу, мы исследовали, как качество вопросов влияет на способности моделей к рассуждениям.

Настройка эксперимента
Мы выбрали три набора задач одинакового размера, но с возрастающей сложностью, и создали для них решения в едином стиле, чтобы сформировать три обучающих набора данных:

  1. Simple-500: 500 простых задач, случайно выбранных из уровней 1 и 2 набора данных MATH.

  2. Complex-500: 500 сложных задач, случайно выбранных из уровней 3, 4 и 5 набора данных MATH.

  3. Advanced-500: 500 продвинутых задач, случайно выбранных из прошлых тестов AIME.

Для подтверждения возрастающей сложности этих наборов мы оценили различные LLM на них, наблюдая снижение точности и увеличение средней длины правильно сгенерированных цепочек рассуждений. Затем мы использовали DeepSeek-R1 для генерации решений (наивысшего качества) для каждого набора задач, которые стали обучающими данными для тонкой настройки модели Qwen2.5-32B-Instruct.

Результаты
Мы оценили все три тонко настроенные модели на бенчмарках AIME2024 и MATH500, чтобы оценить их способности к рассуждениям. Результаты (Рисунок 3) показали следующее:

  • Модель, обученная на наборе Advanced-500, достигла точности 51,5% на сложном бенчмарке AIME2024, что на 16% выше, чем у моделей, обученных на более простых наборах.

  • На бенчмарке MATH500 модель, обученная на Advanced-500, достигла точности 91,2%, несмотря на отсутствие данных для обучения в рамках домена.

Эти результаты свидетельствуют о том, что улучшение способностей к рассуждениям за счет увеличения сложности задач обобщается на различные наборы данных.

Ключевые выводы:

  1. Качество цепочек рассуждений играет критическую роль в производительности моделей. Высококачественные, хорошо структурированные решения (уровень L5) значительно улучшают результаты.

  2. Сложность задач также оказывает существенное влияние. Модели, обученные на более сложных задачах, демонстрируют лучшую производительность на сложных бенчмарках, таких как AIME2024 и MATH500.

  3. Эти результаты подтверждают важность тщательного отбора как задач, так и решений для обучения моделей, что является ключевым принципом подхода LIMO.

Рисунок 3. Сравнение успешности ответов по математике и некоторые контрольные показатели между моделями, обученными на вопросах разного качества: простые-500, сложные-500 и продвинутые-500.
Рисунок 3. Сравнение успешности ответов по математике и некоторые контрольные показатели между моделями, обученными на вопросах разного качества: простые-500, сложные-500 и продвинутые-500.

5.3.3. Исследование 3: Архитектура LLM

Основываясь на нашей гипотезе LIMO, которая подчеркивает важность латентных предварительных знаний в параметрическом пространстве модели, мы исследуем, как различные данные предварительного обучения влияют на способность модели использовать минимальные примеры для математических рассуждений. Это позволяет нам оценить первый ключевой фактор нашей гипотезы: роль предварительно полученных при обучении знаний в активации сложных способностей к рассуждениям.

Настройка эксперимента
Чтобы изолировать влияние предварительного обучения, контролируя архитектуру модели и процедуры тонкой настройки, мы провели эксперименты с двумя 32B-параметрическими вариантами семейства моделей Qwen:

  • Qwen1.5-32B-Chat (Team, 2024a)

  • Qwen2.5-32B-Instruct (базовая модель LIMO).

Обе модели имеют одинаковую архитектуру и количество параметров, но Qwen2.5 демонстрирует значительные улучшения в качестве данных предварительного обучения, особенно в математических и программных данных, по сравнению с предшественником. Мы провели тонкую настройку обеих моделей на идентичных наборах данных LIMO и оценили их производительность на бенчмарках AIME2024 и MATH500.

Результаты
Наши эксперименты показали, что выбор предварительно обученной модели значительно влияет на производительность в задачах рассуждений (Рисунок 4). LIMO, построенная на Qwen2.5-32B-Instruct, значительно превосходит своего предшественника на обоих бенчмарках:

  • На сложном тесте AIME2024 LIMO достигает точности 57,1%, что на 47,1 процентных пункта выше, чем у Qwen1.5-32B-Instruct (10,0%).

  • На MATH500 LIMO демонстрирует исключительную производительность с точностью 94,8%, превосходя Qwen1.5-32B-Instruct на 34,4 процентных пункта.

Эти значительные улучшения свидетельствуют о том, что улучшенное предварительное обучение в Qwen2.5 создает более прочную основу для математических рассуждений. Результаты согласуются с нашей гипотезой LIMO, указывая на то, что более богатые знания в параметрическом пространстве модели позволяют более эффективно использовать минимальные примеры во время файнтюнинга модели.

5.3.4. Кейс обучения

На Рисунке 5 сравниваются ответы, сгенерированные Qwen2.5-32B-Instruct, DeepSeek-R1 и LIMO.

LIMO демонстрирует способности и поведение, сопоставимые с DeepSeek-R1, несмотря на использование минимальных данных и вычислительных ресурсов (всего 817 обучающих примеров). В частности, LIMO показывает сильные способности к саморефлексии и генерации длинных цепочек рассуждений. Например, модель проверяет свои собственные утверждения:

  • "Подождите, 24 минуты — это 0,4 часа? Подождите, нет. Подождите, 60 минут — это 1 час, так что 24 минуты — это 24/60, то есть 0,4 часа".

  • "Но давайте я проверю еще раз. Возможно, я допустил ошибку в расчетах".

Кроме того, LIMO учится выделять дополнительные токены (вычислительные ресурсы) для детального решения сложных уравнений, чтобы избежать ошибок.

В отличие от этого, базовая модель Qwen2.5-32B-Instruct демонстрирует ограничения в процессе рассуждений: она не может исправить неточные утверждения и не проверяет свои расчеты.

Рисунок 4: Влияние выбора предварительно обученной модели на производительность в математических рассуждениях
Рисунок 4: Влияние выбора предварительно обученной модели на производительность в математических рассуждениях

Ключевые выводы:

  1. Качество предварительного обучения играет критическую роль в способности модели к сложным рассуждениям. Модели с более качественными предварительно обученными данными (например, Qwen2.5) показывают значительно лучшие результаты.

  2. LIMO демонстрирует способность к саморефлексии и длинным цепочкам рассуждений, что подтверждает эффективность подхода "Меньше — значит больше".

  3. Эти результаты подчеркивают важность как предварительного обучения, так и тщательного отбора данных для тонкой настройки, что является ключевым принципом LIMO.

Рисунок 5: Сравнение ответов, полученных с помощью Qwen2.5-32B-Instruct, DeepSeek-R1 и LIMO
Рисунок 5: Сравнение ответов, полученных с помощью Qwen2.5-32B-Instruct, DeepSeek-R1 и LIMO

Решение задачи разными моделями (перевод информации на рис.5)

Задача:
Каждое утро Ая отправляется на 9-километровую прогулку и затем останавливается в кофейне. Когда она идет с постоянной скоростью s км/ч, прогулка занимает у нее 4 часа, включая t минут, проведенных в кофейне. Когда она идет со скоростью s + 2 км/ч, прогулка занимает 2 часа 24 минуты, включая t минут в кофейне. Предположим, Ая идет со скоростью s + \frac{1}{2} км/ч. Найдите, сколько минут займет прогулка, включая ( t ) минут, проведенных в кофейне.

(х) Qwen2.5-32B-Instruct

(+) DeepSeek-R1

Чтобы решить задачу, нам нужно
определить время, которое Ая тратит на ходьбу [опущено для краткости...]
Время, затраченное на ходьбу, - это расстояние, деленное на скорость, таким образом, время, затраченное на ходьбу, составляет (9/с) часов. Таким образом,
уравнение для общего времени составляет: 9/с + t = 240,
где t - время, проведенное в кофейне
в минутах.
Далее, мы знаем, что [опущено для
краткости...]. Время, затраченное на ходьбу, составляет 9/(с+2) часов. Таким образом, уравнение для общего времени составляет: 9/(с+ 2) + t = 144. Теперь у нас есть два уравнения: 9/с + t = 240 и 9/(s+2) + t = 144 [для краткости опущено...] Итак, s = 3/16 км/ч. Теперь подставляем s = 3/16 км/ч и вернемся к уравнению (1), чтобы найти t: 9 / (3/16) + t = 240, 48 + t = 240, t = 192 [опущено для краткости...] Таким образом, количество минут, затраченных на прогулку , включая t минут, проведенных в кафе, равно 205.

Хорошо, давайте попробуем разобраться в этой проблеме шаг за шагом.
Итак, Ая каждое утро совершает 9-километровую прогулку, а затем заходит в кафе. [для краткости опущено...]
Сначала давайте разберем проблему. Давайте обозначим ходьбу [опущено для краткости...]. Наша цель - найти общее время, в течение которого она передвигается со скоростью s+1/2 км/ч. Хм, итак, сначала нам нужно определить значения s и t.
Позвольте мне записать уравнения, основанные на данной информации. [для краткости опущено...]
Теперь у нас есть два уравнения: 1) 9/s + t/60 = 4, 2) 9/(s+2) + t/60 = 2,4 Мы можем вычесть второе уравнение из первого, чтобы исключить [опущено для краткости...] Таким образом, левая часть становится: [опущено для краткости...] Умножаем обе части на: [опущено для краткости...] Теперь решим это квадратное уравнение: [опущено для краткости...]. Итак, первоначальная скорость Айи равна 2,5 км/ч. Теперь давайте найдем t [опущено для краткости...]. Подождите, я уточню. Но подождите, в вопросе написано [опущено для краткости...]. Следовательно, ответ должен составлять 204 минуты. Но позвольте мне еще раз проверить, чтобы убедиться, что я не допустил ошибок в квадратном уравнении решение. Давайте проверим правильность значения s = 2.5.[для краткости опущено...]. Таким образом, ответ должен быть 204.

(+) LIMO

Ладно, давайте посмотрим. Итак, каждое утро Ая совершает 9-километровую прогулку, а затем заходит в кафе. Время, которое она тратит на прогулку, включая время, проведенное в кафе, зависит от скорости ее ходьбы. [для краткости опущено...]. Когда она идет немного быстрее, со скоростью s плюс 2 километра в час, общее время составляет 2 часа 24 минуты, что составляет 2 часа 24 минуты, то есть 2,4 часа?
Подождите, 24 минуты - это 0,4 часа? Подождите, нет. Подождите, 60 минут - это 1 час, значит, 24 минуты - это 24/60, то есть 0,4 часа. Итак, 2 часа и 24 минуты - это 2,4 часа. [опущено для краткости...]
Итак, давайте составим уравнения. Обозначим время ходьбы как T1, когда она идет со скоростью s км/ч, и T2, когда она идет со скоростью s+2 км/ч. Тогда T1 + t = 4 часа, а T2 + t = 2,4 часа. Но t указано в минутах, поэтому нам нужно перевести это в часы или перевести общее время в минуты. Возможно, лучше сохранить все в часах. Таким образом, t минут равно t/60 часам. [для краткости опущено...]
Для первого случая: время ходьбы T1 = 9/с часов, тогда общее время T1 + t/60 = 4 часа.
Для второго случая: время ходьбы T2 = 9/(s+2) часов, тогда общее время T2 + t/60 = 2,4 часа. Итак, теперь у нас есть два уравнения: 1) 9/s + t/60 = 4, 2) 9/(s+2) + t/60 = 2,4
Позвольте мне записать это как:
9*(1/s - 1/(s+2)) = 1,6. Таким образом, 1/s - 1/(s+2) = 1,6/9
Позвольте мне вычислить 1,6, деленное на 9. 1,6, деленное на 9, равно 0,1777... или 16/90, что упрощает до 8/45. Итак, 1,6/9 = 8/45 ≈ 0,1777... Итак, 1/s - 1/(s+2) = 8/45
Теперь давайте вычислим левую часть: [опущено для краткости...]. Умножьте обе части на 2: [опущено для краткость...]. Позвольте мне записать это в виде квадратного уравнения: [для краткости опущено...]. Теперь давайте решим это квадратное уравнение: [для краткости опущено...]. Итак, s - это 2,5 км/ч.
Давайте проверим это. Если скорость s равна 2,5 км/ч, то преодоление 9 км займет 9/2,5 = 3,6 часа [опущено для краткости...]
Но позвольте мне проверить еще раз. Возможно, я допустил ошибку в расчетах. Во-первых, s = 2,5 км/ч. Тогда время ходьбы со скоростью s равно 9/2,5 = 3,6 часа…[опущено для краткости...] В качестве альтернативы, мы могли бы иметь перевел все в минуты с самого начала. Давайте посмотрим: Первая прогулка: 9 часов + 10 минут = 4 часа. 4 часа - это 240 минут. [опущено для краткости...] Я думаю, это солидно. Итак, ответ - 204.

Рисунок 5: Сравнение ответов, сгенерированных Qwen2.5-32B-Instruct, DeepSeek-R1 и LIMO
Результаты сравнения ответов, сгенерированных моделями Qwen2.5-32B-Instruct, DeepSeek-R1 и LIMO, подтверждают гипотезу LIMO: с минимальным, но высококачественным набором пост-тренировочных примеров модели могут стать сильными решателями задач.

Количественный анализ

Таблица 4 демонстрирует различия между моделями, обученными на примерах разного качества. Мы наблюдаем общую тенденцию: повышение качества пост-тренировочных примеров приводит к моделям, которые генерируют более длинные ответы с большим количеством строк. Кроме того, модели более высокого качества используют больше саморефлексивных переходов (например, "подождите", "возможно", "может быть", "следовательно"), чтобы выделить дополнительные вычислительные ресурсы для более глубокого анализа.

6. Предыстория и связанные работы

6.1 Эволюция математических рассуждений в LLM

Объемные данные для обучения стали движущей силой развития способностей к рассуждениям в LLM. На этапе предварительного обучения способность к рассуждениям может быть улучшена за счет релевантных корпусов (Wang et al., 2024; Azerbayev et al., 2024; Paster et al., 2023; Shao et al., 2024). Эти корпусы могут включать учебники, научные статьи и математический код, которые отражают разнообразные когнитивные паттерны, используемые для решения задач.

На этапе пост-тренировки ряд исследований сосредоточен на создании крупномасштабных инструктивных данных для обучения LLM рассуждениям (Yue et al., 2023, 2024; Li et al., 2024a). Это включает масштабирование количества вопросов и их решений. Такой подход обещает значительные улучшения производительности, однако способности к рассуждениям, полученные этим методом, критикуются за нацеленность на запоминание фиксированных паттернов, а не на достижение истинного обобщения (Mirzadeh et al., 2024; Zhang et al., 2024).

Например, Mirzadeh et al. (2024) обнаружили, что LLM демонстрируют заметные различия в ответах на разные формулировки одного и того же вопроса, а их производительность снижается при изменении числовых значений в вопросе. Это вызывает сомнения в способности методов SFT (Chu et al., 2025) к обобщению и в том, могут ли LLM быть истинными решателями задач, а не просто системами извлечения знаний (Kambhampati, 2024).

6.2 Масштабирование во время тестирования и длинные цепочки рассуждений

Вместо масштабирования параметров модели и данных для обучения (Kaplan et al., 2020), недавние работы сосредоточились на исследовании масштабирования во время тестирования (OpenAI, 2024; Snell et al., 2024), то есть увеличения количества токенов для улучшения способностей к рассуждениям.

Это может быть достигнуто за счет:

  • Параллельного сэмплирования (Brown et al., 2024; Wang et al., 2022; Li et al., 2022).

  • Символического поиска по дереву (Hao et al., 2023; Chen et al., 2024; Yao et al., 2023).

Кроме того, OpenAI (2024) и Guo et al. (2025) исследуют обучение LLM с использованием обучения с подкреплением для генерации длинных цепочек рассуждений (CoT), которые часто включают саморефлексию, проверку и backtracking — процессы, которые люди используют при решении сложных задач. Наша работа демонстрирует, что эти методы обладают качественными характеристиками, позволяющими выявить присущие LLM способности к логическому мышлению.

Таблица 4: Статистический анализ моделей, обученных на примерах данных разного качества

В таблице представлены три ключевых метрики:

  1. Среднее количество токенов на ответ.

  2. Среднее количество строк на ответ.

  3. Часто встречающиеся ключевые слова в ответах моделей.

Ключевые слова, связанные с переходами в рассуждениях и неопределенностью, выделены жирным шрифтом. Общие стоп-слова (например, "a", "the") исключены для фокусировки на значимых языковых паттернах.

Уровень качества данных

Среднее количество токенов на ответ

Среднее количество строк на ответ

Топ-10 часто встречающихся ключевых слов (в порядке убывания)

Уровень 1

230

9.21

since, however, number, let, thus, which, get, two, triangle, theta

Уровень 2

444.88

50.68

number, need, times, which, find, list, thus, since, triangle, sum

Уровень 3

4956.11

375.60

perhaps, alternatively, consider, number, wait, which, sides, need, equal, seems

Уровень 4

4726.97

354.87

wait, which, number, perhaps, therefore, let, since, maybe, sides, two

Уровень 5

5290.26

239.29

wait, therefore, which, number, since, lets, two, sides, let, maybe

6.3. Эффективность данных в языковых моделях

Zhou et al. (2024a) демонстрируют, что всего с 1000 тщательно отобранных промптов и ответов модели могут научиться следовать определенным форматам и хорошо обобщать их на новые задачи. Эти результаты подчеркивают важность качества, а не количества данных в процессе согласования. Однако остается неясным, можно ли применить этот подход к задачам рассуждений, учитывая потенциально высокую вычислительную сложность таких задач (Merrill and Sabharwal, 2024; Xiang et al., 2025).

Хотя некоторые работы по рассуждениям подчеркивают важность качества данных для обучения (Zhou et al., 2024b), их количество все еще значительно больше, чем в LIMA. Наша работа расширяет идеологию LIMA на задачи рассуждений, исследуя, что составляет высококачественные вопросы и решения, и демонстрирует, что способности LLM к рассуждениям могут быть улучшены с высокой эффективностью использования данных.

7. Будущие исследования

Хотя LIMO демонстрирует впечатляющие успехи в математических рассуждениях с минимальными данными, остается несколько перспективных направлений для дальнейшего изучения.

1. Обобщение на другие домены
Расширение гипотезы LIMO на более широкие области рассуждений представляет собой важный следующий шаг. Хотя наша работа сосредоточена на математических рассуждениях, принципы высококачественных цепочек рассуждений могут быть применимы к научным рассуждениям, логическим выводам и причинно-следственным связям. Понимание того, как эти принципы переносятся между доменами, может выявить универсальные закономерности в эффективных рассуждениях.

2. Теоретические основы
Глубокое теоретическое понимание успеха LIMO также важно. Будущие исследования должны сосредоточиться на формализации взаимосвязи между предварительно обученными знаниями, вычислениями во время вывода и способностями к рассуждениям. Это включает изучение минимального порога знаний для обучения, необходимых для эффективных рассуждений, а также разработку математических моделей для прогнозирования оптимального баланса между качеством и количеством цепочек рассуждений.

3. Автоматизированная оценка
Разработка автоматизированных инструментов для оценки качества цепочек рассуждений представляет собой еще одно важное направление. Текущая ручная оценка качества, хотя и эффективна, требует много времени и сложна для масштабирования. Будущие работы должны быть направлены на создание автоматизированных систем, которые могут оценивать и улучшать качество цепочек рассуждений на основе предложенных нами метрик.

4. Интеграция мультимодальности
Кросс-модальные рассуждения представляют собой захватывающую область для расширения принципов LIMO. Поскольку реальные рассуждения часто включают несколько модальностей, важно исследовать, как визуальная информация и структурированные данные могут улучшить математические способности к рассуждениям.

5. Применение в реальном мире
Применение принципов LIMO к реальным сценариям заслуживает значительного внимания. Будущие исследования должны быть направлены на адаптацию этих подходов к практическим задачам в образовании, научных исследованиях и промышленных приложениях.

6. Связь с когнитивной наукой
Интеграция идей из когнитивной науки может предоставить ценные направления для улучшения. Понимание параллелей между паттернами рассуждений LIMO и когнитивными процессами человека может помочь в разработке более эффективных стратегий рассуждений. Включая изучение того, как различные подходы к построению рассуждений влияют на производительность моделей и их обобщение. Важно включить принципы когнитивной науки в разработку цепочек рассуждений. Такие исследования могли бы не только улучшить системы искусственного интеллекта, но и дать представление о процессах мышления человека. Эти будущие направления в совокупности направлены на углубление нашего понимания эффективного мышления в LLM и расширение их практического применения. Следуя в этом направлении, мы сможем работать над созданием более сложных, эффективных и широко применимых систем мышления, которые лучше удовлетворяют потребности человека в различных областях.

8. Благодарности

Мы выражаем искреннюю благодарность Yixiu Liu и Yiwei Qin за их ценный вклад в эту исследовательскую работу. Их экспертиза, преданность и дух сотрудничества значительно повысили качество нашего исследования. Их проницательные предложения и техническая помощь были ключевыми для достижения наших исследовательских целей.

Мы также благодарим Haoyang Zou и Xuefeng Li за ценные обсуждения на ранних этапах этой работы. Их взгляды и идеи помогли сформировать основу нашего исследования.

Ссылки

[1] Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q. Jiang, Jia Deng, Stella Biderman, and Sean Welleck. 2024. Llemma: An open language model for mathematics.

[2] Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Re, and Azalia ´ Mirhoseini. 2024. Large language monkeys: Scaling inference compute with repeated sampling.

[3] Guoxin Chen, Minpeng Liao, Chengxi Li, and Kai Fan. 2024. Alphamath almost zero: process supervision without process. ArXiv preprint, abs/2405.03553.

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Добавление от автора перевода: некоторые вопросы и ответы, использованные в датасетах обучения:

Датасет aime:

id 75:

Среди 900 жителей Эймвилля 195 владеют бриллиантовым кольцом, 367 — набором для гольфа, а 562 — садовой лопатой. Кроме того, каждый из 900 жителей владеет пакетом конфет в форме сердечек. Есть 437 жителей, которые владеют ровно двумя из этих предметов, и 234 жителя, которые владеют ровно тремя из этих предметов. Найдите количество жителей Эймвилля, которые владеют всеми четырьмя предметами.

Решение:

  1. Пусть ( w, x, y, z ) обозначают количество жителей, которые владеют 1, 2, 3 и 4 предметами соответственно. Мы знаем, что:

    w + x + y + z = 900

    Учитывая, что x = 437 и y = 234 , получаем:

    w + z = 229

  2. Теперь составим уравнение для общего количества предметов. Мы знаем, что есть 195 колец, 367 наборов для гольфа, 562 лопаты и 900 пакетов конфет. Суммируя, получаем 2024 предмета. Таким образом:

    w + 2x + 3y + 4z = 2024

    Подставляя x = 437 и y = 234 , получаем:

    w + 4z = 448

  3. Решаем систему уравнений:

    w + z = 229
    w + 4z = 448

    Вычитая первое уравнение из второго, получаем:

    3z = 219

    z = 73

    Ответ: 73

id 89:

Перевод:

Имеется коллекция из 25 неразличимых белых фишек и 25 неразличимых черных фишек. Найдите количество способов разместить некоторые из этих фишек в 25 ячейках сетки (5 х 5) так, чтобы:

  1. каждая ячейка содержала не более одной фишки;

  2. все фишки в одной строке и все фишки в одном столбце имели одинаковый цвет;

  3. добавление любой дополнительной фишки на сетку нарушило бы одно или несколько из предыдущих условий.

Решение:

  1. Случай 1: Все фишки на сетке имеют одинаковый цвет.

    • В этом случае все ячейки заняты фишками одного цвета.

    • Количество конфигураций в этом случае: 2 (все белые или все черные).

  2. Случай 2: На сетке присутствуют как черные, так и белые фишки.

    • Наблюдение 1: Каждый цвет должен занимать хотя бы один столбец и одну строку.

    • Наблюдение 2: Каждый цвет может занимать не более 4 строк и 4 столбцов.

    • Наблюдение 3: Если фишки одного цвета занимают столбцы с координатами ( {x_1, \dots, x_m} ) и строки с координатами ( {y_1, \dots, y_n} ), то каждая ячейка ((x, y)), где (x \in {x_1, \dots, x_m}) и (y \in {y_1, \dots, y_n}), должна быть занята фишкой этого цвета.

    • Наблюдение 4: Если фишки одного цвета занимают столбцы с координатами ( {x_1, \dots, x_m} ) и строки с координатами ( {y_1, \dots, y_n} ), то каждая ячейка ((x, y)), где (x \notin {x_1, \dots, x_m}) и (y \in {y_1, \dots, y_n}), или (x \in {x_1, \dots, x_m}) и (y \notin {y_1, \dots, y_n}), должна быть пустой.

    • Наблюдение 5: Если фишки одного цвета занимают столбцы с координатами ( {x_1, \dots, x_m} ) и строки с координатами ( {y_1, \dots, y_n} ), то каждая ячейка ((x, y)), где (x \notin {x_1, \dots, x_m}) и (y \notin {y_1, \dots, y_n}), должна быть занята фишкой другого цвета.

    Используя эти наблюдения, количество допустимых конфигураций в этом случае:

    \sum_{n=1}^4 \sum_{m=1}^4 \binom{5}{n} \binom{5}{m} = \left( \sum_{n=1}^4 \binom{5}{n} \right) \left( \sum_{m=1}^4 \binom{5}{m} \right) = (2^5 - 2)^2 = 900.

  3. Итог: Общее количество допустимых конфигураций:

    2 + 900 = 902.

Ответ: 902

id 68:

Алиса и Боб играют в следующую игру. Перед ними лежит стопка из ( n ) фишек. Игроки ходят по очереди, начиная с Алисы. На каждом ходу игрок забирает либо 1 фишку, либо 4 фишки из стопки. Тот, кто забирает последнюю фишку, побеждает. Найдите количество положительных целых чисел ( n ), не превышающих 2024, для которых существует стратегия, гарантирующая Бобу победу независимо от ходов Алисы.

Решение:

  1. Экспериментальный подход:

    • Если ( n = 1 ), Алиса забирает фишку и побеждает.

    • Если ( n = 2 ), Алиса забирает 1 фишку, Боб забирает последнюю и побеждает.

    • Если ( n = 3 ), Алиса забирает 1 фишку, Боб забирает 1 фишку, Алиса забирает последнюю и побеждает.

    • Если ( n = 4 ), Алиса забирает все 4 фишки и побеждает.

    • Если ( n = 5 ), независимо от хода Алисы, Боб может забрать последние фишки и победить.

    Заметим, что Алиса выигрывает при ( n = 1, 3, 4 ), а Боб выигрывает при ( n = 2, 5 ).

  2. Стратегия Боба:

    • Если ( n ) кратно 5, Боб может всегда выиграть. Например, если Алиса берет 1 фишку, Боб берет 4, и наоборот. Это продолжается до тех пор, пока не останется 0 фишек.

    • Если ( n \equiv 2 \mod 5 ), Боб также может выиграть, используя аналогичную стратегию. Например, если ( n = 7 ), Алиса берет 1, Боб берет 4, и так далее, пока не останется 2 фишки. Алиса вынуждена взять 1, и Боб забирает последнюю.

  3. Подсчет количества подходящих ( n ):

    • Количество чисел, кратных 5, не превышающих 2024: ( \left\lfloor \frac{2024}{5} \right\rfloor = 404 ).

    • Количество чисел, дающих остаток 2 при делении на 5, не превышающих 2024: ( \left\lfloor \frac{2024 - 2}{5} \right\rfloor + 1 = 405 ).

    • Общее количество подходящих ( n ): ( 404 + 405 = 809 ).

Ответ: 809

id 82:

Список положительных целых чисел обладает следующими свойствами:

  • Сумма элементов списка равна 30.

  • Уникальная мода списка равна 9.

  • Медиана списка — это положительное целое число, которое не появляется в самом списке.

Найдите сумму квадратов всех элементов списка.

Решение:

  1. Анализ условий:

    • Третье условие подразумевает, что размер списка должен быть четным числом, так как если бы он был нечетным, медиана списка обязательно присутствовала бы в списке.

    • Рассмотрим возможные четные размеры списка.

  2. Случай 1: Размер списка равен 2.

    • Единственный возможный список: ([9, 9]).

    • Сумма элементов: (9 + 9 = 18), что не удовлетворяет первому условию (сумма должна быть 30).

    • Этот случай не подходит.

  3. Случай 2: Размер списка равен 4.

    • В списке должно быть два числа 9 (так как 9 — уникальная мода).

    • Оставшаяся сумма: (30 - 18 = 12).

    • Остальные два числа должны быть различными и их сумма должна быть равна 12.

    • Пусть эти числа имеют вид (a) и (12 - a).

    • Оба числа должны быть меньше 9, чтобы медиана не была равна 9 (так как медиана в списке из 4 элементов — это среднее значение двух центральных чисел).

    • Единственная подходящая пара: (5) и (7).

    • Таким образом, список: ([5, 7, 9, 9]).

  4. Проверка условий:

    • Сумма: (5 + 7 + 9 + 9 = 30).

    • Мода: 9 (появляется дважды).

    • Медиана: (\frac{7 + 9}{2} = 8), которая не присутствует в списке.

    • Все условия выполнены.

  5. Вычисление суммы квадратов:
    [
    5^2 + 7^2 + 9^2 + 9^2 = 25 + 49 + 81 + 81 = 236.
    ]

Ответ: 236

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  • 10.04.25 23:26 patricialovick86

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  • 10.04.25 23:27 patricialovick86

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  • 11.04.25 00:48 patricialovick86

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  • 11.04.25 03:20 edwardoelliott6

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  • 11.04.25 08:10 Beatrice Gallagher

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  • 13.04.25 01:13 michaeldavenport218

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  • 13.04.25 01:13 michaeldavenport218

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  • 18.04.25 20:39 michaeldavenport218

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  • 18.04.25 20:39 michaeldavenport218

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  • 20.04.25 17:22 patricialovick86

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  • 20.04.25 17:22 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 21.04.25 03:51 gleerandon

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  • 21.04.25 12:19 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]

  • 24.04.25 17:59 cynthia19morris

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  • 24.04.25 18:04 Marcus Sandford

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  • 24.04.25 18:14 davidjustin50

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  • 24.04.25 18:23 joshuawashington

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  • 25.04.25 15:44 Sharo2025

    HACKER FOR CRYPTO SCAM RECOVERY SERVICE CONTACT //PASSCODE CYBER RECOVERY

  • 25.04.25 15:46 Sharo2025

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  • 26.04.25 19:01 ashlyncarson

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  • 27.04.25 02:41 elizabethrush89

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  • 29.04.25 12:10 walterkeith2004

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  • 30.04.25 16:39 ratty clara

    I’ve been a victim of a scam, lost all my money to a broker I invested with, was depressed for a few months, but the whole story changed when I visited Trustpilot. I came across a review about a man, Mr Bogdan sovar, on helping people get back their lost investment. I contacted him because I needed some help in getting my money back. To my greatest surprise, I was able to get my money back after a few days of getting in touch with him. It was all free, all he required for was a testimony of her generosity, which I promised I would do in all platforms. You can reach him at his Gmail address: hackerrone90 @ gmail . com and will guide you on the steps to take and get your invested capital, including your bonus, back

  • 30.04.25 22:56 thomaslilley99

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  • 01.05.25 06:40 armand231101

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  • 01.05.25 14:46 kookersylvia81

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  • 01.05.25 20:59 elizabethrush89

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  • 03.05.25 05:01 melissaholroyd

    "The breakthrough came when they traced the stolen BTC to a lesser-known Pakistani exchange. By collaborating with Interpol and Pakistani authorities, they managed to freeze the exchange account that held the bulk of my stolen coins. Although 0.3 BTC had already been liquidated, 3.2 BTC ($128,000) was successfully recovered and returned to me within 12 days. As for the people behind the scam, the click farm’s operators are now facing fraud and money laundering charges. I’m incredibly grateful for the hard work of Tech Cyber Force Recovery. They didn’t just help me recover my funds, they sent a clear message that scams like this won’t go unpunished. It’s a reminder that while the crypto space can be risky, there are Tech Cyber Force recovery teams out there who will fight to bring justice. WhatsApp +1 561 726 36 97 telegram (@)Techcyberforc

  • 03.05.25 12:24 ratty clara

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  • 08.05.25 03:35 jwright70

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  • 13.05.25 19:18 canemarcus0

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  • 13.05.25 20:21 wendytaylor015

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  • 14.05.25 14:45 Clarkanderson

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  • 14.05.25 14:45 Clarkanderson

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  • 16.05.25 14:18 patricialovick86

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  • 17.05.25 18:33 marywilliam3544

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  • 19.05.25 05:42 kylebro

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  • 20.05.25 11:40 robertalfred175

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  • 20.05.25 11:40 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 26.05.25 17:55 Juliuslaurence1

    It started as an ordinary Tuesday. I was transferring 5 BTC my life savings at the time to a new cold wallet for better security. My fingers moved swiftly, copying the address, pasting it, and hitting Send without a second thought. Then ,the horrow struck.A split second after the transaction confirmed, I realized I had pasted the wrong address. A typo. One wrong character. My stomach twisted into knots as I watched my Bitcoin disappear into the blockchain abyss, headed to an address I didn’t recognize. Panic set in. 5 BTC. Over $300,000 at the time. Gone. I spent sleepless nights scouring forums, reaching out to blockchain experts, even pleading with miners but the immutable nature of Bitcoin meant no one could reverse it. The address was active, but the owner? Unknown. Then, I stumbled upon Washington Recovery Pro, a firm specializing in cryptocurrency transaction reversals and asset recovery. Skeptical but desperate, I reached out. Their team, led by a seasoned blockchain investigator named Marcus, took my case. They explained that while Bitcoin transactions are irreversible, if the recipient is identifiable, recovery is possible through negotiation or legal pressure. Using on-chain forensics, they traced the mistaken transaction to a crypto exchange wallet meaning the recipient had likely cashed out or moved the funds. Washington Recovery Pro contacted the exchange, providing irrefutable proof of the erroneous transfer. After weeks of pressure, the exchange froze the funds and initiated a recovery process. two weeks after my heart-stopping mistake, I received an email: Your 5 BTC has been recovered and will be returned to your wallet. I couldn’t believe it. Washington Recovery Pro had done the impossible. My Bitcoin was saved, but not by luck by skill, persistence, and legal expertise. If you ever face a similar nightmare, remember: some mistakes can be undone. Email: [email protected]: +1 (903) 249‑8633‬

  • 30.05.25 05:03 kevinpatmore2

    Good day everyone, some statements people make may appear unbelievable, yet they reflect their truth due to their personal experiences. I'm a truck driver and I enjoy playing the lottery, but it's been 8 years without winning anything significant. I became upset over this and chose to seek help online from anyone skilled in using spells to win the lottery since my grandma had faith in spells and a story about Lord Meduza spells drew my interest. I obtained his contact information, and we discussed all aspects of how he could assist me. In less than 72 hours, Lord Meduza provided me with the lottery numbers I required after he completed the spell for me. I purchased my ticket online, followed the guidance of Lord Meduza, and now my life is fortunate because I won a $42.5 million Jackpot in the lottery I participated in. I’m thankful to Lord Meduza because he truly keeps his promises and I will donate $1 million to the orphanage homes in my city. Email: [email protected] or WhatsApp +18079072687.

  • 01.06.25 17:55 springthorpecameron5

    After losing $153,000 to a fake investment platform on Telegram, I found myself under immense pressure due to both the financial loss and my ongoing cancer treatment. Desperate to recover my stolen cryptocurrency, I researched viable options and discovered Ruder Cyber Tech Sleuths, a registered Bitcoin and USDT Recovery Agency with a strong reputation. Their impressive record of successful recoveries and positive reviews gave me hope. Despite my initial doubts, reaching out to them was a pivotal moment in my recovery journey. The process was complex, but their dedication, advanced technology, and professionalism were evident. I highly recommend their services to anyone who has fallen victim to scams. Ruder Cyber Tech Sleuths are truly exceptional and provide hope in an often dishonest world. [email protected] [email protected] whatsapp: +12132801476 Telegram : @rudercybersleuths

  • 02.06.25 16:01 wendytaylor015

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  • 02.06.25 16:01 wendytaylor015

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  • 03.06.25 22:21 [email protected]

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  • 06.06.25 18:43 jack67667766

    Jasmine Lopez is an expert in retrieving stolen cryptocurrency, including Ethereum and USDT. Her effective methods make her a reliable ally for theft victims. One client, who lost €908,000, sought her assistance, and Jasmine managed to recover the full amount within a day, bringing immense relief to the client. Jasmine is dedicated to helping others in similar situations and is available to offer support. For help with recovering lost funds, you can contact her via email at [email protected] or on WhatsApp at ‪+44 736 644 5035‬.

  • 08.06.25 12:49 jack67667766

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  • 09.06.25 19:49 garnierroux

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  • 21.06.25 04:37 jamesgrant

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  • 23.06.25 15:44 michaeldavenport218

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  • 23.06.25 15:44 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 24.06.25 04:10 Beth Wilson

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  • 24.06.25 04:23 Beth Wilson

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  • 25.06.25 22:56 rennebrandon

    I am forever grateful to the amazing team at Dune Nectar Web Expert. They did what I thought was never possible. They played a crucial role in helping me recover all my lost funds from a fraudulent forex and crypto trading scheme, including the pråofits I thought I'd earned. Looking back, I realize I was a bit of a fool for trusting greedy and deceitful brokers with my hard-earned money. However, I'm overjoyed that I found Dune Nectar Web Expert. They are a team of honest and highly skilled professionals available for hire. Dune Nectar Web Expert helps individuals and organizations recover stolen cryptocurrencies and digital assets. They helped me heal every penny I lost and provided me with the right signals and a reliable platform to trade with. Thanks to Dune Nectar Web Expert, I'm earning more than ever, and I couldn't be happier. That's why I can't stop sharing my positive experience and praising their amazing services and expertise. If you're still struggling with failures in binary options, crypto, or forex trading, or if you're looking to recover your lost USDC, Bitcoin, Ethereum, or other crypto funds, I strongly advise you to reach out to: Telegram>>> ( T.me/dunenectarwebexpert ) Mail>>>> Support (@) Dunenectarwebexpert (.) Com Web>>> https://dunenectarwebexpert.com/

  • 26.06.25 20:33 paulmargaret

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  • 27.06.25 00:46 paulmargaret

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  • 27.06.25 22:31 rennebrandon

    I am forever grateful to the amazing team at Dune Nectar Web Expert. They did what I thought was never possible. They played a crucial role in helping me recover all my lost funds from a fraudulent forex and crypto trading scheme, including the pråofits I thought I'd earned. Looking back, I realize I was a bit of a fool for trusting greedy and deceitful brokers with my hard-earned money. However, I'm overjoyed that I found Dune Nectar Web Expert. They are a team of honest and highly skilled professionals available for hire. Dune Nectar Web Expert helps individuals and organizations recover stolen cryptocurrencies and digital assets. They helped me heal every penny I lost and provided me with the right signals and a reliable platform to trade with. Thanks to Dune Nectar Web Expert, I'm earning more than ever, and I couldn't be happier. That's why I can't stop sharing my positive experience and praising their amazing services and expertise. If you're still struggling with failures in binary options, crypto, or forex trading, or if you're looking to recover your lost USDC, Bitcoin, Ethereum, or other crypto funds, I strongly advise you to reach out to: Telegram>>> ( T.me/dunenectarwebexpert ) Mail>>>> Support (@) Dunenectarwebexpert (.) Com Web>>> https://dunenectarwebexpert.com/

  • 28.06.25 00:46 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 28.06.25 00:46 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 29.06.25 07:39 paulmargaret

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  • 29.06.25 11:52 wendytaylor015

    My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 30.06.25 15:06 wendytaylor015

    My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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