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[Перевод] Законы масштабирования – архитектура O1 Pro // Инфраструктура синтетических данных, RLAIF, токеномика вычислений

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С каждым днем растут страхи и сомнения относительно законов масштабирования ИИ. Большинство предсказателей отрасли ИИ утверждают об окончании законов масштабирования, которые за последние несколько лет привели к мгновенному улучшению возможностей крупных языковых моделей (LLM). К ним присоединились журналисты, вооружившись неопределенной информацией о неспособности моделей успешно масштабироваться из-за предполагаемой низкой производительности. Критики также указывают на насыщенность существующих бенчмарков, где новые модели демонстрируют лишь незначительные улучшения, а также на исчерпание доступных данных для обучения и замедление аппаратного масштабирования.

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Несмотря на эту тревогу, увеличение капитальных затрат на строительство крупных лабораторий искусственного интеллекта говорит само за себя. Amazon, инвестирующая значительные суммы в ускорение Trainium2, планы Meta по созданию центров обработки данных мощностью 2 ГВт в 2026 году в Луизиане, агрессивные планы Google по обучению нескольких центров обработки данных для преодоления ограничений мощности на одном объекте — все эти ключевые лица, по-видимому, непоколебимы в своей убежденности в том, что законы масштабирования все еще живы. Почему?

Масштабирование обучения, новые и старые парадигмы продолжаются

Выпуск OpenAI o1 доказал полезность и потенциал моделей рассуждений, открыв новые неизведанные измерения для масштабирования. Но это не единственный метод, который обеспечивает значимые улучшения в производительности модели по мере масштабирования вычислений. Другие методы, которые обеспечивают улучшения модели с большим количеством вычислений, включают генерацию синтетических данных, оптимизацию проксимальной политики (PPO), функциональные верификаторы и другую инфраструктуру обучения для рассуждений. Масштабирование меняется и развивается, и вместе с этим продолжает ускоряться весь процесс разработки ИИ. Переход от неисправных контрольных показателей к более сложным позволит лучше измерять прогресс. В этом отчете мы опишем старую тенденцию масштабирования до обучения, а также новые тенденции масштабирования. Это означает, что новые методы потребуют еще больше времени обучения для масштабирования вычислений, чем предполагалось ранее.

Мы рассмотрим архитектуру OpenAI o1 и o1 Pro как с точки зрения инфраструктуры обучения, так и с точки зрения токеномики вывода, включая стоимость, масштабирование KVCache, пакетирование и многое другое. Мы также обсудим ведущие синтетические данные AI Lab и инфраструктуру RL. Также мы хотим прояснить ситуацию с «провалами» Claude 3.5 Opus от Anthropic и Orion от OpenAI, а также с тем, какие планы масштабирования будут реализованы.

Масштабирование поет оды величайшему закону масштабирования вычислений — закону Мура

Сегодняшние дебаты о законах масштабирования ИИ не отличаются от многолетних дебатов вокруг масштабирования вычислений и закона Мура. Любой, кто попытается измерить вычислительную мощность ЦП по тактовой частоте — распространенной метрике, использовавшейся до конца 2000-х годов, примерно во время окончания масштабирования Деннарда — будет утверждать, что с тех пор мы вообще не добились никакого прогресса. Но на самом деле, вычислительная мощность постоянно совершенствовалась — когда мы достигли предела в тактовой частоте процессоров, фокус сместился на многоядерные архитектуры и другие методы повышения производительности, несмотря на ограничения плотности энергии и охлаждения.

Источник: Плотность транзисторов ЦП, тактовая частота, мощность и производительность с 1970 по 2015 год — Чарльз Леггетт
Источник: Плотность транзисторов ЦП, тактовая частота, мощность и производительность с 1970 по 2015 год — Чарльз Леггетт

Конец закона Мура — еще одно препятствие, с которым столкнулась полупроводниковая промышленность, но в последнее время эти дебаты стихли, поскольку пионеры ИИ, такие как Nvidia, обеспечили огромный прирост вычислений за счет масштабирования по нескольким совершенно новым измерениям. Усовершенствованная упаковка позволила продолжить прогресс в вычислениях за счет масштабирования ввода/вывода (I/O) и позволила чипам использовать общую площадь кремния за пределами ограничений. Параллельные вычисления внутри и между чипами и создание более крупных сетевых доменов с высокой пропускной способностью позволили чипам лучше работать вместе в масштабе, особенно для вывода.

Источник: Nvidia
Источник: Nvidia

Как и компьютерные энтузиасты в 2004 году, сегодняшние основные аналитики и журналисты не видят леса за деревьями: несмотря на замедление одной тенденции, отрасль в целом продолжает двигаться вперед с головокружительной скоростью из-за других новых появляющихся парадигм, которые созрели для масштабирования. Предварительное обучение станет лишь одним из векторов улучшения, а совокупный «закон масштабирования» продолжит масштабироваться так же, как закон Мура на протяжении последних 50 лет.

Проблемы масштабирования предварительного обучения

Масштабирование предварительного обучения обеспечило значительный прирост производительности модели, но есть несколько препятствий, на преодолении которых отрасль в настоящее время сосредоточена.

Одним из очевидных препятствий является то, что данные становится все труднее собирать — данные в Интернете не расширяются со скоростью, пропорциональной вычислительным ресурсам. Вот почему сегодняшние мегамодели с триллионами параметров оказались меньше оптимальных для Chinchilla — имеют гораздо меньшее количество токенов обучения по сравнению с параметрами модели.

Масштабирование Chinchilla относится к оптимальному увеличению данных по сравнению с количеством параметров относительно увеличения вычислительных ресурсов. Недостаток данных приводит к тому, что модель плохо обобщает, в то время как избыток данных приводит к переобучению и тратам вычислительных ресурсов. В некоторых случаях отклонение от оптимального соотношения имеет смысл: модели с избыточным обучением (например, GPT-4o и Llama) могут значительно снизить затраты на вывод и являются предпочтительными для поставщиков, которые имеют большую базу пользователей для обслуживания этой модели.

В январе 2023 года, до запуска GPT-4, мы писали о практических ограничениях масштабирования и о том, как GPT-4 планировала их преодолеть. С тех пор модели колебались от более чем Chinchilla Optimal (гораздо больше данных, чем параметры модели) до менее чем Chinchilla Optimal (когда данные стали ограниченными). Ограничения вычислительных мощностей в прошлом преодолевались за счет улучшения аппаратного обеспечения для обучения и вывода.

На сегодняшний день полезные источники данных, такие как учебники и документация, полностью исчерпаны, и то, что осталось, в основном представляет собой текстовые источники данных низкого качества. Кроме того, веб-данные по-прежнему представляют собой узкое распределение данных, и моделям нужно больше данных, чтобы продолжать обобщать. Поскольку модели сложнее масштабировать оптимальным образом, предварительное обучение становится более сложным.

Кроме того, если лаборатории обучают модели с недостаточным количеством данных по мере их масштабирования, модели становятся чрезмерно параметризованными, становясь неэффективными и приводя к большому объему запоминания, а не обобщения. Вместо этого лаборатории обращаются к более широкому использованию синтетических данных, чтобы облегчить эту проблему.

Однако эта проблема в меньшей степени относится к основным лабораториям искусственного интеллекта. Meta имеет примерно в 100 раз больше данных, чем в общедоступном Интернете (при условии, что они могут использовать эти данные в соответствии с нормативными требованиями). Это может дать им преимущество в продолжении масштабирования с меньшим количеством проблем, чем у других. На YouTube ежедневно загружается 720 000 новых часов видео, и нам кажется, что AI Labs только начали рассматривать возможность обучения на огромном объеме данных, содержащихся в видео. Это в дополнение к их способности генерировать качественные синтетически сгенерированные данные, архитектуру которых мы обсудим позже.

Для обучения на квадриллионах альтернативных токенов, доступных из видео, требуется огромное масштабирование общего FLOP обучения, которое будет обеспечено аппаратными инновациями и системной инженерией. Например, масштабирование еще на один порядок величины FLOP обучения потребует обучения в нескольких центрах обработки данных, поскольку необходимое количество ускорителей больше не может поместиться в пределах одного центра обработки данных. Проект Rainier предлагает Amazon предоставить Anthropic 400 тысяч чипов Tranium 2, но в сыром FLOP это меньше 100 тысяч GB200. Anthropic придется добиться значительных инженерных достижений, чтобы провести обучение в таком кластере. Распространение ускорителей по большому кампусу или нескольким кампусам само по себе приводит к значительным проблемам, вызванным законом Амдаля, хотя уже существует несколько предложений для решения этой проблемы.

Другим ограничением в отношении параметров масштабирования является экономика вывода. Лаборатории ИИ могут капитализировать огромные суммы инвестиций в обучение больших моделей и амортизировать использование модели как для большой и растущей базы пользователей, так и для внутренних вариантов использования для разработки дальнейших итераций модели. Когда дело доходит до вывода, они должны быть осторожны, чтобы не выводить на рынок модели, которые слишком дороги или неэкономичны для обслуживания.

Оценки также не являются всеобъемлющими. Есть множество возможностей или свойств моделей, которые существующие оценки не охватывают должным образом. Передача обучения, когда модель становится лучше в домене за счет изучения чего-то другого, и контекстное обучение — это области, где необходимо разрабатывать еще больше оценок. И наконец, всегда будут варианты конечного использования, которые трудно предсказать заранее, но которые приносят конечным пользователям огромную выгоду.

То, что измеряется, всегда улучшается.

Новые, более сложные оценки

Появились новые оценки, направленные на лучшую дифференциацию моделей и сосредоточенные на непосредственном решении конкретных полезных приложений. SWE-Bench, например, является одним из важнейших бенчмарков, направленных на решение проверенных людьми задач из открытых репозиториев Python на GitHub. Новый Claude 3.5 Sonnet достиг результата 49% (State of the Art) на SWE-Bench Verified, тогда как большинство моделей существенно отстают.

Еще один пример — бенчмарк, оценивающий возможности AI в R&D, который некоторые считают «самой важной характеристикой для отслеживания». Research Engineering Benchmark (RE) состоит из семи сложных и открытых исследовательских сред машинного обучения. Люди, как правило, лучше справляются с оценками в течение более длительных временных горизонтов, но в течение 2-часового временного горизонта лучшие агенты ИИ набрали балл в 4 раза выше, чем люди. Важные задачи, подобные описанным выше, в которых в настоящее время доминируют люди, являются идеальной основой для масштабирования вычислений времени вывода. Мы ожидаем, что модели, которые лучше используют эту форму масштабирования, в будущем превзойдут людей.

Источник: RE-Bench: Оценка возможностей передовых исследований и разработок ИИ агентов языковых моделей по сравнению с экспертами-людьми

Еще одна тенденция заключается в том, что оценки включают чрезвычайно сложные вопросы экспертного уровня. Два ярких примера — Graduate-Level Google-Proof Q&A Benchmark (GPQA) и Frontier Math. GPQA состоит из 448 вопросов с несколькими вариантами ответов по химии, биологии и физике. Для сравнения, OpenAI выяснила, что люди экспертного уровня (т. е. люди с докторской степенью) набрали ~70% на GPQA Diamond, а o1 набрал 78% на том же наборе. В прошлом году GPT-4 с поиском (и методом цепочки размышлений при отказе) набрал 39% на GPQA Diamond.

Еще один пример тенденции к использованию чрезвычайно сложных вопросов — FrontierMath (FM). FM — это бенчмарк из сотен оригинальных математических вопросов, на решение которых у людей могут уйти часы и даже дни. Он охватывает широкий спектр математических тем, включая теорию чисел, вещественный анализ и т. д. Особенностью этой оценки является то, что она не публикуется, и это сводит к минимуму риск загрязнения данных; в процессе может участвовать автоматизированный верификатор, что упрощает процесс оценки.

Источник: FrontierMath: бенчмарк для оценки расширенных математических рассуждений в ИИ

Лучшая производительная модель в этом бенчмарке составляет 2%, но лаборатории ожидают, что этот показатель значительно улучшится. В среднесрочной перспективе Anthropic может достичь 80% на FrontierMath.

Последующее обучение: новая область масштабирования

Предварительное обучение находится в центре дебатов относительно законов масштабирования, поскольку его легко понять, но это только одна часть жизненного цикла ИИ. После того, как модель предварительно обучена, еще предстоит проделать значительную работу по ее подготовке к использованию. Целью предварительного обучения является, в очень узком смысле, «правильно спрогнозировать следующий токен». Достижение этого все еще оставляет нас далеко от конечной цели развития LLM, которая заключается в «ответе на запросы пользователя» или «выполнении задачи».

Прежде чем погрузиться в то, как работает и как была создана модель OpenAI O1 Pro, мы сделаем обзор Supervised Fine-Tuning (SFT), обучения с подкреплением (RL) и синтетических данных.

Supervised Fine-Tuning

Supervised Fine-Tuning (SFT) — наиболее известный тип постобучения. В модели отображается тщательно подобранный набор данных пар входных и выходных данных, а «демонстрационные данные» охватывают конкретную область (например, код, математические вычисления, выполнение инструкций и т. д.). В отличие от предварительной подготовки, здесь важнее качество данных, чем их количество. Из-за меньшего объема данных процесс дообучения требует меньше вычислительных ресурсов.

Изначальный успех GPT был обусловлен использованием тщательно отобранных образцов данных, созданных и размеченных людьми, предоставленных компаниями вроде Scale AI. Однако со временем сгенерированные человеком данные с трудом масштабируются.

Неотъемлемая роль синтетических данных в постобучении

Самой важной задачей в SFT является создание достаточно больших и высококачественных наборов данных в желаемых областях. Это позволяет модели лучше работать в определенных областях, таких как код, математика, рассуждения; благодаря передаче обучения имеются побочные эффекты, делая модель лучше и в других областях. Очевидно, что модели с сильными математическими и кодировочными навыками лучше справляются с общими рассуждениями, но это распространяется и на другие области — модели, обученные на китайском и английском языках, лучше справляются с английским, чем те, которые были обучены только на английском. Синтетические данные открыли измерение, в котором высококачественные данные могут быть получены с использованием контролируемой и немасштабируемой методологии для дообучения моделей на любые темы, если есть возможность их создать.

Интенсивное использование синтетических данных также стимулирует стремление к лучшим моделям. Например, OpenAI первой разработала GPT-4 и использовала её для создания лучших синтетических наборов данных, чем другие поставщики моделей — до тех пор, пока у них не появились модели аналогичного уровня. Именно это позволило многим открытым моделям и лабораториям из Китая быстро догнать лидеров, поскольку они обучались на синтетических данных от GPT-4.

Чем лучше базовая модель справляется с задачами оценки, тем лучше набор данных для обучения. Этому свойственны свои собственные законы масштабирования. Вот как мы получили «новый Claude 3.5 Sonnet». Anthropic завершил обучение Claude 3.5 Opus, и он показал себя хорошо, масштабируясь соответствующим образом (не обращайте внимания на сторонников отрицания масштабирования, которые утверждают обратное).

Однако Anthropic не выпустил его. Это связано с тем, что вместо публичного выпуска Anthropic использовал Claude 3.5 Opus для генерации синтетических данных и моделирования вознаграждений, чтобы значительно улучшить Claude 3.5 Sonnet наряду с пользовательскими данными. Затраты на вывод кардинально не изменились в отличие от производительности модели. Зачем выпускать 3.5 Opus, если с точки зрения затрат это экономически невыгодно по сравнению с выпуском 3.5 Sonnet с дальнейшим постобучением из указанного 3.5 Opus?

С большим количеством синтетических данных появляются лучшие модели. Лучшие модели предоставляют лучшие синтетические данные и действуют как лучшие судьи для фильтрации или оценки предпочтений. При использовании синтетических данных существует множество более мелких законов масштабирования, которые в совокупности подталкивают к более быстрой разработке лучших моделей.

Примеры синтетических данных

Выборка отбраковки

Пример области, где синтетические данные активно используются, — это генерация наборов данных кода. Обычно это делается путем обозначения различных задач программирования или подсказок в качестве начальных значений и побуждения модели генерировать вопросы, относящиеся к этим задачам.

Затем модель просят сгенерировать набор потенциальных решений. Решения, которые проходят соответствующие тесты или могут выполняться правильно, добавляются к обучающему набору данных, эффективно отфильтровывая некачественные образцы в процессе, называемом выборкой отбраковки (Rejection Sampling). Выборка отбраковки является важной частью процесса генерации синтетических данных, поскольку она гарантирует, что набор данных достаточно высокого качества, чтобы быть ценным во время Supervised Fine-Tuning (SFT) или обучения с подкреплением (Reinforcement Learning). Однако в результате многие из сгенерированных токенов выбрасываются, так как генерация синтетических данных требует много вычислений.

Эта методология построения синтетического набора данных для дообучения была принята многими крупными AI-лабораториями и используется для дообучения моделей Gemini, GPT, Llama и Claude.

Но выборка отбраковки может быть сложнее, чем кажется. В случае Llama модель была вынуждена пересмотреть свой ответ, если первоначальный ответ был неверным, и модель давала правильный ответ со второй попытки в 20% случаев. В качестве еще одной иллюстрации полезности синтетических данных команда Meta перевела код Python на PHP, обеспечив качество с помощью синтаксического анализа и выполнения, и ввела эти дополнительные данные в набор данных SFT, чтобы учесть отсутствие общедоступного кода PHP. Это эффективно демонстрирует, что синтетические данные используются для надежной и предсказуемой генерации полезных данных для недостаточно представленных областей.

Источник: Meta
Источник: Meta

Оценка модели другими моделями

Еще одна тенденция — использовать другую LLM в качестве судьи. В качестве выборки отбраковки Meta использовала другую, более раннюю версию Llama 3, выступая в качестве судьи для кода, который не был строго исполняемым (т. е. псевдокода), и оценивая результаты по принципу "зачет/незачет" с точки зрения корректности и стиля кода. В некоторых случаях выборка отбраковки выполняется с помощью различных моделей, работающих одновременно для оценки моделей. Хотя это обходится дешевле, чем человеческие данные, достаточно трудно справиться с таким набором автоматических судей.

Здесь важно отметить для всех методов выборки отбраковки — чем лучше модель «судьи», тем выше качество полученного набора данных. Несмотря на то, что этот цикл обратной связи был введен Meta в производство в этом году, он в течение года или двух до этого использовался Anthropic и OpenAI.

Длинные контекстные наборы данных

Другим примером использования синтетических данных являются размеры контекстов. Модели предварительно обучаются с ограниченными размерами контекста, поскольку большинство данных уже имеют малую длину контекста, а также потому что большая длина последовательности требует большего объема памяти для хранения кэшированных значений (KV Cache), что делает развертывание инфраструктуры обучения еще сложнее, чем оно есть. Такие модели, как Gemini, GPT и Claude, изначально предварительно обучаются с меньшей длиной последовательности, а затем впоследствии постобучаются для добавления более длинных контекстов.

Обычно людям сложно аннотировать длинные примеры контекста в данных SFT, поскольку существуют ограничения человеческих ресурсов для предоставления качественной аннотации. Чтение длинных фрагментов текста занимает много времени и утомительно. Синтетические данные появились как полезный и надежный способ решения этой проблемы.

Один из методов создания синтетических данных большой длины контекста заключается в использовании модели из более ранней контрольной точки и суммировании ею больших фрагментов текста, разбитых на фрагменты размером ее (в настоящее время небольшой) длины контекста. Эти сводки, включающие смоделированные вопросы и ответы, затем могут быть использованы для создания массива синтетических данных, которые затем могут быть использованы в SFT.

Другие примеры включают генерацию синтетических данных для прохождения сложных тестов, таких как задачи "поиск иголки в стоге сена". Существует множество более сложных типов синтетических данных, предназначенных для обучения моделей обобщению и пониманию информации в различных частях расширенного контекста.

Обучение с подкреплением

Обучение с подкреплением (RL) является ведущим методом для согласования и улучшения моделей.

Обучение с подкреплением (RL) — это когда агент (например, крупная языковая модель) обучается выполнять определенные действия и добиваться определенных результатов, максимизируя вознаграждения, которые даются либо за эти конкретные действия, либо за достижение определенного результата. Есть две стороны касательно RL: источник обратной связи и способ её использования. Первая касается того, как получать сигналы, а вторая — того, как использовать эти сигналы для обновления модели.

С подкреплением обучения крупная языковая модель, которую мы пытаемся оптимизировать, играет роль агента, который может выполнять набор действий, учитывая входные данные, и получать различные вознаграждения в зависимости от действия, которое он выполняет. Мы оптимизируем поведение этого агента в отношении наших целей подкрепления обучения, заставляя агента изучать действия, которые могут максимизировать ожидаемое кумулятивное вознаграждение.

Существует несколько основных подходов к интеграции обратной связи и определению действий, которые выполняет агент, — с использованием методов, на основе ценности (Value-based) или методов на основе политики (Policy-based), таких как оптимизация предпочтений (Direct Preference Optimization) и оптимизация политики в доверительной области (Trust Region Policy Optimization, TRPO), а также методы актёр-критик (Actor-Critic), которые комбинируют методы на основе политики и ценности. Proximal Policy Optimization (PPO) является ярким примером модели актера-критика, а более сложные ее вариации являются основным методом RL во всех основных лабораториях ИИ.

Методы, основанные на ценностях, определяют ценность достижения заданного состояния и определяют значения для каждого возможного состояния. Каждому состоянию присваивается значение, основанное на ожидаемой доходности, которую агент может получить, начав с этого состояния, а затем определяет свое действие на каждом шаге, основываясь на ценности каждого действия, доступного ему. Исторически, методы, основанные на ценностях, чаще использовались в RL, но современные приложения гораздо более эффективно работают с методами на основе политики.

Источник: Huggingface
Источник: Huggingface

В методах на основе политики агент управляется функцией политики, которая определяет набор действий для данного состояния, и назначает распределение вероятностей для этого набора действий. Действия, которые должны быть выполнены в данном состоянии, могут быть детерминированными, то есть нахождение в каждом состоянии всегда приведет к одному и тому же действию, или стохастическими, где распределение вероятностей описывает потенциальные действия в данном состоянии. Затем функция политики обучается направлять агента к действиям, которые максимизируют ожидаемое вознаграждение.

Источник: Huggingface
Источник: Huggingface

При использовании методов на основе политики в обучении с подкреплением, модель может либо оценить конечный результат данной задачи, чтобы определить вознаграждение в случае модели вознаграждения за результат (ORM), либо определить вознаграждение, оценивая каждый отдельный шаг в данном процессе, в случае модели вознаграждения за процесс (PRM). Использование PRM может быть особенно полезным при обучении моделей рассуждений - в то время как ORM может обнаружить, что цепочка рассуждений привела к неправильному ответу, PRM может сообщить вам, на каком шаге цепочки была ошибка.

Поскольку функция политики направляет действия агента на каждом шаге, она также является особенно полезной структурой для оптимизации поведения агентов/моделей на промежуточных шагах процесса вывода.

Модели вознаграждения за результат и модели вознаграждения за процесс часто используются в алгоритме Proximal Policy Optimization (PPO), который широко применяется в обучении с подкреплением для поэтапного улучшения модели политики с целью максимизации суммарного вознаграждения и оптимизации LLM для достижения заданной цели. Использование ORM и PRM с PPO особенно важно при обучении многошаговых моделей рассуждений, которые в настоящее время являются ключевым направлением в сообществе. Ниже мы опишем, как это делается для o1 Pro.

Proximal Policy Optimization (PPO)

Proximal Policy Optimization (PPO) может использоваться как для выравнивания, так и для тонкой настройки (Fine-Tuning), но она гораздо лучше подходит и чаще используется во время обучения с подкреплением, используемого во время выравнивания.

В контексте PPO, «Policy» (Политика) относится к использованию модели политики для определения действий агента или модели, «Proximal» (Приближённость) указывает на методологию алгоритма, заключающуюся в том, что политика обновляется постепенно, а «Optimization» (Оптимизация) — это процесс поэтапного улучшения политики с использованием обратной связи от модели вознаграждения для улучшения модели политики, что, в свою очередь, оптимизирует ожидаемое кумулятивное вознаграждение.

Выше мы в основном обсуждали методы, основанные на политиках, но PPO включает в свою реализацию как методы, основанные на политиках, так и методы, основанные на ценностях. Таким образом, можно сказать, что PPO использует метод актера-критика. Актер управляется моделью, основанной на политике, которая определяет, какое действие предпринять для данного состояния (т. е. метод, основанный на политиках), а Критик оценивает предпринятое действие в соответствии с функцией ценности (метод, основанный на ценностях). Таким образом, Актер и Критик работают вместе итеративным образом.

Следовательно, максимизация целевой функции PPO подтолкнет политику в направлении предпочтения действий, которые соответствуют более высокому значению функции преимущества.

RLHF

Обучение с подкреплением и обратной связью от человека (RLHF) является основным методом согласования LLM; оно сделало LLM полезными и стало ведущим фактором для взрывного роста ChatGPT. Обычно оно использует обучение на основе политики, когда модель вознаграждения, которая обучается на основе обратной связи от человека, используется для обновления политики, которая управляет поведением модели.

С помощью RLHF люди-аннотаторы просматривают выборку ответов на подсказки и ранжируют свои предпочтения для одного ответа по сравнению с другим. Целью здесь является сбор важных данных о том, какие ответы люди предпочитают. Эти данные о предпочтениях затем используются для обучения модели вознаграждения, которая пытается угадать среднее предпочтение маркировщика для заданного вывода модели. Другими словами, обученная модель вознаграждения действует как Критик в структуре «Актер-Критик».

Обученная модель вознаграждения оценивает это действие по сравнению с человеческими предпочтениями, на которых она обучена, и насколько лучше или хуже действие по сравнению со средним действием. Затем обратная связь от этой модели вознаграждения действует для согласования модели Актера, гарантируя, что она выполняет действия (генерирует токены) в соответствии с желаемой политикой.

Как обсуждалось выше, PPO используется для итеративного обновления функции политики языковой модели, обеспечивая стабильное обучение при предотвращении резких изменений в политике. Крупномасштабный PPO для лабораторий ИИ использует несколько взвешенных моделей вознаграждения для определенных аспектов, таких как полезность, правдивость и безопасность.

В целом, RLHF позволяет моделям лучше справляться с задачами, которые волнуют реальных пользователей и по которым они предоставили данные о своих предпочтениях. Llama 2-Chat от Meta достигла гораздо лучшей производительности по таким факторам, как полезность и безвредность после раундов RLHF. Дополнительные вычисления, используемые для масштабирования моделей во время RL, дают четкие результаты. Потенциальные преимущества использования синтетических данных вместо обратной связи, созданной человеком, и большее полагание на ИИ для обратной связи также могут оправдать использование ещё большего объёма вычислений.

Источник: Meta
Источник: Meta

Однако у RLHF есть существенные ограничения. Во-первых, выполнение всего жизненного цикла RLHF может быть очень медленным, поскольку необходимо время, чтобы предоставить различные сгенерированные ответы людям-респондентам, обычно через компанию ИИ, вставляющую такие подсказки для обратной связи при обслуживании своих моделей.

Даже при большой базе пользователей сбор большого объема данных о предпочтениях сложен и дорог — Meta потратила от 10 до 20 миллионов долларов на данные о предпочтениях для Llama 2, что больше, чем стоимость самого времени вычислений.

RLHF по своей сути сложно масштабировать, особенно в областях, где нет огромного количества существующих данных. Аннотация человеком также является дорогой. Именно поэтому многие AI-компании переходят к использованию обучения с подкреплением с обратной связью от ИИ (RLAIF) в процессе обучения.

Более крупные компании, занимающиеся искусственным интеллектом, имеют здесь явное преимущество. Claude, Gemini и ChatGPT просят пользователей оставить отзыв об ответах моделей, которые они размещают. Например, иногда ChatGPT просит выбрать, какой из двух ответов вы предпочитаете. Этот процесс фактически бесплатно собирает лучший источник обратной связи (непосредственно от пользователей). Поскольку OpenAI имеет огромную клиентскую базу из более чем 300 миллионов пользователей, он может собирать достаточно обратной связи для улучшения моделей.

Поставщики с меньшим количеством пользователей или использующие платформу, которая менее благоприятна для предоставления пользователями обратной связи, должны прибегать к другим методам, таким как DPO вместо PPO. Прямая оптимизация предпочтений (DPO) — еще один метод, часто обсуждаемый вместе с RLHF, хотя большинство технически не относят его к методу обучения с подкреплением.

DPO полностью отказывается от обучения модели вознаграждения и вместо этого использует оптимизацию для прямой корректировки политики, чтобы максимизировать вероятность того, что политика заставляет модель выдавать предпочтительные результаты на основе данных о предпочтениях человека. Оптимизация работает с использованием бинарной кросс-энтропийной потери, которая сравнивает отношения вероятностей между текущей моделью и эталонной моделью (обычно той же моделью до тонкой настройки). DPO гарантирует, что модель учится отдавать предпочтение желаемым ответам, оставаясь при этом близкой к поведению эталонной модели.

Более простой подход, используемый в DPO, может достигать сопоставимых или лучших результатов, чем RLHF с использованием полной модели вознаграждения, при этом он менее подвержен сбоям и проще в реализации. Ярким примером достоинств этого подхода является то, что Llama 3 не подвергалась RLHF и прошла через DPO. Meta обнаружила, что в случае Llama 3 DPO была более эффективной и стабильной, чем PPO, и использовала меньше вычислений. Но использование DPO означает, что качество набора данных предпочтений имеет первостепенное значение, заслуживая особого внимания к тому, как эти данные собираются и обрабатываются.

Источник: Meta
Источник: Meta

В конечном итоге Meta извлекла урок, который другие лаборатории уже знали: DPO не масштабируется так же хорошо, как PPO, — и чтобы продолжить улучшать свое постобучение, они должны обратиться к RLAIF. Это было продемонстрировано в релизе новейшей LLAMA 3.3.

RLAIF

Вместо того, чтобы полагаться на обратную связь человека для обучения модели вознаграждения, обучение с подкреплением с обратной связью ИИ (RLAIF) заменяет обратную связь человека другой моделью. Модель вознаграждения обучается на основе обратной связи, генерируемой ИИ — обычно это некая форма модели или алгоритма оценки, которая будет оценивать данные завершения и определять вознаграждение соответствующим образом.

Источник: RLAIF против RLHF: масштабирование обучения с подкреплением на основе обратной связи человека с обратной связью ИИ

В общем, ничего принципиально нового по сравнению с RLHF в RLAIF нет, но это вносит значительные изменения. Аннотации могут быть выполнены быстро, а запросы могут быть сгенерированы синтетически, чтобы инициировать обучение модели с подкреплением в тех областях, где требуется дополнительное обучение или данные.

Кроме того, предоставление обратной связи по типичным задачам в области математики, науки и общего знания, RLAIF также позволяет быстро генерировать обратную связь для решения более тонких задач, таких как этические дилеммы, культурные нормы и социальные взаимодействия, а также ранжировать эти ответы с помощью другой LLM. Это позволяет обеспечить более широкий охват тем для выравнивания модели и дает возможность тренерам моделей быстро начать обучение по этим темам, не ожидая сбора человеческой обратной связи.

Уникальным применением RLAIF является конституционный ИИ от Anthropic. Конституционный ИИ работает в два этапа. На первом этапе базовая модель критикует и пересматривает свои собственные результаты в соответствии с набором конституционных принципов, написанных людьми. Эти первоначальные ответы могут быть токсичными или бесполезными. Затем ответы непрерывно пересматриваются с использованием различных принципов из конституции. Это создает набор данных пар пересмотра и подсказок, которые затем используются для тонкой настройки модели с помощью метода контролируемого дообучения (SFT).

Второй этап процесса для конституционного ИИ похож на RLHF, но без данных о предпочтениях человека, обеспечивающих обратную связь относительно безвредности. ИИ оценивает пары ответов из модели предыдущего этапа в соответствии с конституционными принципами, которые по сути являются моделями множественного вознаграждения. Сгенерированные ИИ предпочтения безвредности объединяются с данными обратной связи человека для полезности, чтобы обучить гибридную модель предпочтений («гибридное» означает, что модель включает человеческие данные). Наконец, модель с первого этапа дообучается с использованием RL, где эта модель предпочтений служит сигналом вознаграждения.

Самым примечательным наблюдением этого подхода является то, что он масштабируется во многих различных областях — если есть модель, которая хороша в ранжировании ответов на основе того, какой из них более точный с научной точки зрения, то в дополнение к способности определять безвредность, эта модель может использоваться для оптимизации для научно точных ответов.

Источник: Конституционный ИИ от Anthropic: Безвредность через обратную связь от ИИ

RL также является ключевой частью разработки моделей рассуждений, которые используют цепочку мыслей (CoT).

Модели рассуждений и цепочка мыслей (CoT)

Математика — это фундаментальная логика и рассуждения в инженерии, строительстве и проектировании систем. Математика выделяется как целевая дисциплина для тонкой настройки моделей, поскольку на продвинутых уровнях сложности у тренеров моделей нет достаточно сложных подсказок. Один из способов решения этой проблемы — платить высококвалифицированным людям за создание подсказок или их генерацию внутри компании. Эффективное решение математических задач с помощью рассуждений требует четко сформулированной и правильной цепочки мыслей, на которой может учиться модель.

Некоторые математические возможности могут быть улучшены с помощью таких инструментов, как доступ к интерпретатору кода, позволяющий моделям генерировать и выполнять код на таких языках, как Python, который может помочь в решении некоторых математических задач, но кода недостаточно для решения многих задач, особенно самых сложных математических задач. В настоящее время огромное количество усилий направлено на обучение моделей рассуждений решению сложных математических задач.

Модели можно побуждать генерировать цепочки мыслей из коробки, но результаты могут быть ненадежными, поскольку ошибка на одном шаге цепочки приведет к неправильному конечному решению. Однако в o1 Pro есть несколько мер предосторожности, чтобы предотвратить это. Другая проблема заключается в том, что даже новейшие модели могут галлюцинировать и фальсифицировать информацию, что может легко усугубить ошибку на одном из этапов рассуждения.

Модель, настроенная на проведение рассуждения с использованием цепочки мыслей, может решить многие из вышеперечисленных проблем. В этом подходе обучение с подкреплением используется для согласования поведения модели с подходом цепочки мыслей.

Этот процесс применяет обучение с подкреплением для согласования поведения базового LLM с подходом цепочки мыслей и повышения его точности с использованием нескольких других отдельных моделей и LLM.

Первый независимый LLM, который мы обсудим, — это Generator, который обучен генерировать решения, обоснованные на нескольких этапах. Как правило, Generator отделен от базового LLM, поскольку он настроен специально для задачи генерации этих шагов рассуждения, в то время как базовый LLM обычно настроен для общих задач.

Второй LLM — это Модель верификатора, которая отвечает за оценку того, являются ли решения, созданные Generator, правильными или нет, и предоставляет соответствующее вознаграждение.

Модели верификаторов можно обучать с помощью человеческой аннотации, посредством автоматической аннотации процессов или с помощью автоматических верификаторов. В статье OpenAI «Давайте проверим шаг за шагом» исследователи представили набор данных PRM800K для наблюдения за процессом, в котором люди, размечающие данные, аннотируют 800 000 этапов процесса, которые являются частью 75 000 решений на 12 000 вопросов из математического набора данных, которые выводятся генератором, как обсуждалось в статье.

Источник: Давайте проверим шаг за шагом

Сбор таких аннотаций связан с немалыми затратами. В одной статье по математике несколько студентов университета, которым дали час на решение 20 задач, набрали от 40% до 90%, причем 90%-ный результат получил трехкратный золотой медалист IMO. В статье OpenAI отмечается, что из-за высокой стоимости было бы непрактично создать достаточно большой набор данных, ориентированный на аннотацию процессов (PRM), чтобы сопоставить его по объёму с набором данных, ориентированным на результаты (ORM), для прямого сравнения этих подходов.

Альтернативой является использование автоматической аннотации процесса или поиск автоматических верификаторов.

Автоматические верификаторы — это система или модель, которая в идеале может быстро и легко проверить, является ли решение заданной задачи правильным. Для кода это может быть просто фактическое выполнение стоимости для проверки того, что он дает желаемые результаты, в то время как для математики это может быть оценка заданной функции или использование доказывающего средства, такого как LEAN, для проверки корректности. Но использование автоматических верификаторов может быть не таким «автоматическим», как кажется — создание зависимостей от внешних систем может добавить накладные расходы, которые могут снизить эффективность обучения. Еще автоматические верификаторы иногда могут потребовать времени для запуска.

Автоматическая аннотация процесса может генерировать эту пошаговую аннотацию процесса. Вместо того, чтобы человек оценивал промежуточный шаг, модель Completer используется для создания нескольких различных путей шагов рассуждения. В статье «Math-Shepherd» используется автоматическая аннотация процесса — генерируется ряд путей, а затем эти пути оцениваются либо путем маркировки процесса как хорошего шага рассуждения, если он приводит к правильному окончательному ответу (т. е. жесткая оценка — Hard Estimation), либо путем назначения баллов на основе частоты, с которой шаг приводит к правильному решению (т. е. мягкая оценка — Soft Estimation).

Источник: Math-Shepherd: пошаговая проверка и укрепление LLM без человеческих аннотаций

Четвертой моделью является модель вознаграждения, которая обучается на основе меток аннотации процесса.

Подводя итог нашему предыдущему объяснению, можно сказать, что существует два типа моделей вознаграждения: те, которые предоставляют вознаграждение на основе результата, модель вознаграждения за результат (ORM), и те, которые предоставляют вознаграждение на основе процесса, модели вознаграждения за процесс (PRM). ORM обычно работают, ранжируя множество различных ответов, которые предоставляет модель, а затем выбирая наиболее ранжированный. PRM, напротив, оценивают и присваивают оценку каждому шагу цепочки рассуждений и предоставляют вознаграждение на основе этой оценки, и по этой причине они обычно являются предпочтительными при обучении моделей цепочки мыслей. В статье «Давайте проверим шаг за шагом» продемонстрированы более сильные результаты для PRM по сравнению с ORM. С учетом сказанного, OpenAI по-прежнему больше полагается на ORM.

Источник: Давайте проверим шаг за шагом

В «Math-Shepherd» обучение с подкреплением через Proximal Policy Optimization (PPO) используется для обучения итоговой языковой модели (LLM) желаемому поведению в формате цепочки рассуждений.

Масштабирование на этапе вывода

Релиз предварительной версии OpenAI o1 привлек внимание отрасли к появлению нового закона масштабирования — чем больше вычислений во время теста (т. е. вычислений во время вывода), тем лучше ответ, и попытки использовать это измерение масштабирования находятся на переломном этапе.

При получении запросов, будь то простые или сложные вопросы, традиционные LLM будут непрерывно генерировать токены, не отслеживая промежуточные шаги, пока не решат, что достигли ответа.

Но как объяснялось выше, модели рассуждений, напротив, разбивают ответ на дискретное количество шагов рассуждений, называемых цепочкой мыслей, прежде чем предоставить ответ пользователю. Модели рассуждений могут откатываться назад, если они приходят к нелогичному выводу, признавая, что была допущена ошибка или определенный подход зашел в тупик, и пересматривать предыдущие шаги, чтобы вернуть цепочку рассуждений на правильный путь.

Выпуск моделей рассуждений имеет два важных последствия: во-первых, значительное улучшение производительности модели для сложных оценок, и во-вторых, осознание того, что это улучшение производительности модели масштабируется с вычислениями во время теста и надежно распространяется на LLM.

Источник: OpenAI
Источник: OpenAI

Масштабирование во время теста — не новая концепция. В настольных играх и покере идея расширения вычислений во время теста существует уже некоторое время. Например, AlphaGo, система DeepMind для игры в Go, во время теста использует поиск по дереву Монте-Карло (Monte Carlo Tree Search), чтобы решить, какие ходы следует использовать. Если лишить ее возможности поиска во время вывода, её рейтинг Elo упадет с ~5200 до 3000 (уровень лучших людей — около ~3800). Вычисления во время вывода позволили достичь сверхчеловеческих достижений в Go.

С большими вычислениями модели рассуждений могут продумывать больше шагов и повышать вероятность получения правильного ответа. Сегодня возможности рассуждений ограничены возможностями систем вывода, поскольку большая длина контекста, необходимая для моделей рассуждений, значительно увеличивает требования к памяти и вычислениям.

Это означает, что операторы систем вывода для моделей рассуждений ограничивают длину цепочек рассуждений, чтобы поддерживать разумную длину контекста и снизить затраты, обеспечивая при этом экономически выгодное обслуживание достаточного числа пользователей с приемлемой задержкой токен-к-токену. Из этого следует, что сегодняшние модели рассуждений работают с одной рукой, связанной за спиной, и они могут значительно масштабироваться по производительности, поскольку на рынке появляются более мощные системы вывода, такие как GB200 NVL72. Когда это станет экономически выгодным, разрешение o1 регулировать длину своей цепочки рассуждений и используемых вычислений станет эффективным способом реализовать потенциал масштабирования вычислений на этапе вывода.

Источник: OpenAI
Источник: OpenAI

Как мы видим из оценок и графика ниже, с одной попытки GPT-4o превосходит другие модели. Самый наивный способ масштабировать вычисления во время теста — увеличить количество одновременно запускаемых образцов. В статье «Большие языковые обезьяны» говорится, что простое повторение выборки может масштабировать вычисления на этапе вывода и давать гораздо лучшие результаты.

Источник: Большие языковые обезьяны: масштабирование вычислений вывода с повторной выборкой

Возможно, это один из самых основных способов выполнения поиска. Генерация большего количества образцов обеспечивает больший охват, что определяется как вероятность получения правильного ответа хотя бы в одном из образцов (т. е. pass@k). Можно утверждать, что предоставление меньшим моделям возможности многократно размышлять над одной задачей может быть более точным и экономически выгодным решением. Однако для успешного выполнения этой задачи потребуется эффективный верификатор, который сможет определить, когда нам удалось метафорически создать полное собрание сочинений Шекспира.

«Это были лучшие времена, это были самые туманные времена»; Источник: Симпсоны
«Это были лучшие времена, это были самые туманные времена»; Источник: Симпсоны

Масштабирование вычислений на этапе вывода через поиск

Поиск — это еще одно измерение масштабирования, которое не используется в OpenAI o1, но используется в o1 Pro. o1 не оценивает множественные пути рассуждений во время теста (т. е. во время вывода) и не проводит никакого поиска вообще. В видео Саши Раш «Рассуждения о масштабировании вычислений на этапе вывода (o1)» говорится о полезном обсуждении и иллюстрации поиска, а также затрагиваются другие темы, связанные с моделями рассуждений.

Самосогласованность/голосование большинства — это одна из методологий поиска, в которой мы запускаем подсказку через модель несколько раз, тем самым генерируя несколько ответов, а затем выбираем правильный ответ, который появляется чаще всего среди заданного количества образцов.

Источник: Саша Раш
Источник: Саша Раш

Best-of-N Sampling — еще один метод, при котором мы генерируем N решений для конкретной подсказки и затем используем модель-верификатор для идентификации цепочек рассуждений, которые привели к правильному ответу. Этот подход обычно ограничен областями, где возможна верификация (например, судоку, но не эссе) и зависит от эффективности модели-верификатора.

Источник: Саша Раш
Источник: Саша Раш

Monte Carlo roll-outs — этот метод основан на Best-of-N. Здесь мы оцениваем заданный промежуточный шаг, генерируя несколько путей для завершения цепочки мыслей, начиная с этого промежуточного шага. Эта оценка может помочь нам решить, продолжать ли этот шаг или двигаться вперед с предполагаемым будущим шагом, улучшая нашу общую цепочку мыслей.

Мы обсудили основы RL, синтетические данные, цепочки мыслей, вычисления на этапе вывода и другие концепции; в следующий раз мы рассмотрим, что OpenAI сделала с o1 и o1 Pro как во время обучения, так и во время вывода. Мы также обсудим токеномику вычислений на этапе вывода, включая стоимость, масштабирование KV Cache, пакетирование и многое другое. И наконец, мы объясним, что OpenAI будет делать дальше с Orion и почему слухи о его провале не соответствуют действительности.

Источник

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  • 18.01.25 12:41 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]

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  • 22.01.25 21:43 DoraJaimes23

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  • 23.01.25 02:37 [email protected]

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  • 23.01.25 14:20 nellymargaret

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  • 26.01.25 03:54 [email protected]

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  • 06.02.25 19:42 Marta Golomb

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  • 08.02.25 05:45 [email protected]

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  • 13.02.25 16:50 andytom798

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  • 13.02.25 16:52 birenderkumar20101

    I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected])

  • 13.02.25 17:37 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 14.02.25 02:50 Vladimir876

    I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected]) or W.H.A.T.S.A.P.P:+1.7.6.0.9.2.3.7.4.0.7

  • 14.02.25 02:56 christophadelbert3

    Я был в полном смятении, когда потерял все свои сбережения, инвестируя в криптовалюту. Со мной связалась онлайн женщина по электронной почте, выдавая себя за менеджера по работе с клиентами банка, которая сказала мне, что я могу удвоить свои сбережения, инвестируя в криптовалюту. Я никогда не думал, что это будет мошенничество, и я потеряю все. Это продолжалось неделями, пока я не понял, что меня обманули. Вся надежда была потеряна, я был опустошен и разорен, к счастью для меня, я наткнулся на статью в моем местном бюллетене о CYBERPUNK RECOVERY Bitcoin Recovery. Я связался с ними и предоставил всю информацию по моему делу. Я был поражен тем, как быстро они вернули мои криптовалютные средства и смогли отследить этих мошенников. Я действительно благодарен за их услуги и рекомендую CYBERPUNK RECOVERY всем, кому нужно вернуть свои средства. Настоятельно рекомендую вам связаться с CYBERPUNK, если вы потеряли свои биткойны USDT или ETH из-за инвестиций в биткойны Электронная почта: ([email protected]) W.h.a.t.s.A.p.p (+.1.7.6.0.9.2.3.7.4.0.7)

  • 14.02.25 02:56 christophadelbert3

    I was in total dismay when I lost my entire savings investing in cryptocurrency, I was contacted online by a lady through email pretending to be an account manager of a bank, who told me I could make double my savings through cryptocurrency investment, I never imagined it would be a scam and I was going to lose everything. It went on for weeks until I realized that I have been scammed. All hope was lost, I was devastated and broke, fortunately for me, I came across an article on my local bulletin about CYBERPUNK RECOVERY Bitcoin Recovery, I contacted them and provided all the information regarding my case, I was amazed at how quickly they recovered my cryptocurrency funds and was able to trace down those scammers. I’m truly grateful for their service and I recommend CYBERPUNK RECOVERY to everyone who needs to recover their funds urge you to contact CYBERPUNK if you have lost your bitcoin USDT or ETH through bitcoin investment Email: ([email protected]) WhatsApp (+17609237407)

  • 14.02.25 15:33 prelogmilivoj

    I never imagined I would find myself in a situation where I was scammed out of such a significant amount of money, but it happened. I became a victim of a fake online donation project that cost me over $30,000. It all started innocently enough when I was searching for assistance after a devastating fire incident in California. While looking for support, I came across an advertisement that seemed to offer donations for fire victims. The ad appeared legitimate, and I reached out to the project manager to inquire about how to receive the donations. The manager was very convincing and insisted that in order to qualify for the donations, I needed to pay $30,000 upfront. In return, I was promised $1 million in donations. It sounded a bit too good to be true, but in my desperate situation, I made the mistake of believing it. The thought of receiving a substantial amount of help to rebuild after the fire clouded my judgment, and I went ahead and sent the money. However, after transferring the funds, the promised donations never arrived, and the manager disappeared. That’s when I realized I had been scammed. Feeling lost, helpless, and completely betrayed, I tried everything I could to contact the scammer, but all my efforts were in vain. Desperation led me to search for help online, hoping to find a way to recover my money and potentially track down the scammer. That’s when I stumbled upon several testimonies from others who had fallen victim to similar scams and had been helped by a company called Tech Cyber Force Recovery. I reached out to them immediately, providing all the details of the scam and the information I had gathered. To my immense relief, the experts at Tech Cyber Force Recovery acted swiftly. Within just 27 hours, they were able to locate the scammer and initiate the recovery process. Not only did they help me recover the $30,000 I had lost, but the most satisfying part was that the scammer was apprehended by local authorities in their region. Thanks to Tech Cyber Force Recovery, I was able to get my money back and hold the scammer accountable for their actions. I am incredibly grateful for their professionalism, expertise, and dedication to helping victims like me. If you have fallen victim to a scam or fraudulent activity, I highly recommend contacting Tech Cyber Force Recovery. They provide swift and efficient recovery assistance, and I can confidently say they made all the difference in my situation. ☎☎ 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ ☎☎ 📩 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ 📩

  • 14.02.25 22:12 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com, WhatsApp Info: 1 (252) 378-7611

  • 16.02.25 01:01 Peter

    I fell victim to a crypto scam and lost a significant amount of money. What are the most effective strategies to recover my funds? I've heard about legal actions, contacting authorities, and hiring recovery experts, but I'm not sure where to start. Can you provide some guidance on the best ways to recover money lost in a crypto scam? Well if this is you, [email protected] gat you covered get in touch and thank me later

  • 16.02.25 20:06 eunice49954

    Agent Lopez specializes in recovering stolen cryptocurrencies, especially Bitcoin/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 18.02.25 19:35 donovancristina

    Now, I’m that person sharing my success story on LinkedIn, telling others about the amazing team at TECH CYBER FORCE RECOVERY who literally saved my financial life. I’ve also become that guy who proudly shares advice like “Always back up your wallet, and if you don’t have TECH CYBER FORCE RECOVERY on speed dial.” So, a big thank you to TECH CYBER FORCE RECOVERY if I ever get a chance to meet the team, I might just offer to buy them a drink. They’ve earned it. FOR CRYPTO HIRING WEBSITE WWW://techcyberforcerecovery.com WHATSAPP : ⏩ wa.me/15617263697

  • 18.02.25 22:13 keithphillip671

    WhatsApp +44,7,4,9,3,5,1,3,3,8,5 Telegram @Franciscohack The day my son uncovered the truth—that the man I entrusted my hopes of wealth and companionship with through a cryptocurrency platform was a cunning scammer was the day my world crumbled. The staggering realization that I had been swindled out of 150,000.00 Euro worth of Bitcoin left me in a state of profound despair. As a 73-year-old grappling with loneliness, I had sought solace in what I believed to be a genuine connection, only to find deceit and betrayal. Countless sleepless nights were spent in tears, mourning not only the financial devastation but also the crushing blow to my trust. Attempts to verify the authenticity of our interactions were met with hostility, further deepening my sense of isolation. Through the loss it was my son who became my beacon of resilience. He took upon himself the arduous task of tracing the scam and seeking justice on my behalf. Through meticulous effort and determination, he unearthed {F R A N C I S C O H A C K}, renowned for their expertise in recovering funds lost to cryptocurrency scams. Entrusting them with screenshots and evidence of the fraudulent transactions, my son initiated the journey to reclaim what had been callously taken from me. {F R A N C I S C O H A C K} approached our plight with empathy and unwavering professionalism, immediately instilling a sense of confidence in their abilities. Despite my initial skepticism, their transparent communication and methodical approach reassured us throughout the recovery process. Regular updates on their progress and insights into their strategies provided much-needed reassurance and kept our hopes alive amid the uncertainty. Their commitment to transparency and client welfare was evident in every interaction, fostering a sense of partnership rather than mere service. Miraculously, in what felt like an eternity but was actually an impressively brief period, {F R A N C I S C O H A C K} delivered the astonishing news—I had recovered the entire 150,000.00 Euro worth of stolen Bitcoin. The flood of relief and disbelief was overwhelming, marking not just the restitution of financial losses but the restoration of my faith in justice. {F R A N C I S C O H A C K} proficiency in navigating the intricate landscape of blockchain technology and online fraud was nothing short of extraordinary. Their dedication to securing justice and restoring client confidence set them apart as more than just experts—they were steadfast allies in a fight against digital deceit. What resonated deeply with me {F R A N C I S C O H A C K} integrity and compassion. Despite the monumental recovery, they maintained transparency regarding their fees and ensured fairness in all dealings. Their proactive guidance on cybersecurity measures further underscored their commitment to safeguarding clients from future threats. It was clear that their mission extended beyond recovery—it encompassed education, prevention, and genuine advocacy for those ensnared by cyber fraud. ([email protected]) fills me with profound gratitude. They not only rescued my financial security but also provided invaluable emotional support during a time of profound vulnerability. To anyone navigating the aftermath of cryptocurrency fraud, I wholeheartedly endorse {F R A N C I S C O H A C K}. They epitomize integrity, expertise, and unwavering dedication to their clients' well-being. My experience with {F R A N C I S C O H A C K} transcended mere recovery—it was a transformative journey of resilience, restoration, and renewed hope in the face of adversity.

  • 21.02.25 07:42 daniel231101

    I never thought I would fall victim to a crypto scam until I was convinced of a crypto investment scam that saw me lose all my entire assets worth $487,000 to a crypto investment manager who convinced me I could earn more from my investment. I thought it was all gone for good but I kept looking for ways to get back my stolen crypto assets and finally came across Ethical Hack Recovery, a crypto recovery/spying company that has been very successful in the recovery of crypto for many other victims of crypto scams and people who lost access to their crypto. I’m truly grateful for their help as I was able to recover my stolen crypto assets and get my life back together. I highly recommend their services EMAIL ETHICALHACKERS009 AT @GMAIL DOT COM whatsapp +14106350697

  • 21.02.25 21:38 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 22.02.25 18:01 benluna0991

    Mark Zuckerberg. That’s the name I was introduced to when I first encountered the cryptocurrency mining platform, WHATS Invest. A person claiming to be Zuckerberg himself reached out to me, saying that he was personally backing the platform to help investors like me earn passive income. At first, I was skeptical—after all, how often do you get a direct connection to one of the world’s most famous tech entrepreneurs? But this individual seemed convincing and assured me that many people were already seeing substantial returns on their investments. He promised me a great opportunity to secure my financial future, so I decided to take the plunge and invest $10,000 into WHATS Invest. They told me that I could expect to see significant returns in just a few months, with payouts of at least $1,500 or more each month. I was excited, believing this would be my way out of financial struggles. However, as time passed, things didn’t go according to plan. Months went by, and I received very little communication. When I finally did receive a payout, it was nowhere near the $1,500 I was promised. Instead, I received just $200, barely 13% of what I had expected. Frustrated, I contacted the support team, but the responses were vague and unhelpful. No clear answers or solutions were offered, and my trust in the platform quickly started to erode. It became painfully clear that I wasn’t going to get anywhere with WHATS Invest, and I began to worry that my $10,000 might be lost for good. That's when I discovered Certified Recovery Services. Desperate to recover my funds, I decided to reach out to them for help. In just 24 hours, they worked tirelessly to recover the majority of my funds, successfully retrieving $8,500 85% of my initial investment. I couldn’t believe how quickly and efficiently they worked to get my money back. I’m extremely grateful for Certified Recovery Servicer's fast and professional service. Without them, I would have been left with a significant loss, and I would have had no idea how to move forward. If you find yourself in a similar situation with WHATS Invest or any other platform that isn’t delivering as promised, I highly recommend reaching out to Certified Recovery Services They were a lifesaver for me, helping me recover nearly all of my funds. It's reassuring to know that trustworthy services like this exist to help people when things go wrong. They also specialize in recovering money lost to online scams, so if you’ve fallen victim to such a scam, don’t hesitate to contact Certified Recovery Services they can help! Here's Their Info Below: WhatsApp: +1(740)258‑1417 mail: [email protected], [email protected] Website info; https://certifiedrecoveryservices.com

  • 23.02.25 22:00 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 06:36 ANDREW DAVIS

    RECOVER YOUR SCAMMED FUNDS AND CRYPTOCURRENCY VIA SPOTLIGHT RECOVERY Professional hackers at Spotlight Recovery provide services for compromised devices, accounts, and websites as well as for recovering stolen bitcoin and money from scams. They finish their work safely and quickly. Their order has been fulfilled since day one, and the victim will never be conscious of the outside entrance. Very few even attempt to give critical information, look into network security, or discreetly discuss personal issues. The Spotlight Recovery Crew helped me recover $264,000 that was stolen from my corporate bitcoin wallet, and I appreciate them giving me further details on the unidentified people. In the event that you have been defrauded of your hard-earned cash or bitcoins, contact SPOTLIGHT RECOVERY CREW at Contact: [email protected]

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 27.02.25 12:46 monikaguttmacher

    Weddings are supposed to be magical, but the months leading up to mine were anything but. Already, wedding planning was a high-stress, sleep-deprived whirlwind: endless details to manage, from venue deposits and guest lists to dress fittings and vendor contracts. But nothing-and I mean, nothing-compared to the panic that washed over me when I realized that somehow, I had lost access to my Bitcoin wallet-with $600,000 inside. It happened in the worst possible way. In between juggling my to-do lists and trying to keep my sanity intact, I lost my seed phrase. I went through my apartment like a tornado, flipping through notebooks, checking every email, every file-nothing. I sat there in stunned silence, heart pounding, trying to process the fact that my entire savings, my security, and my financial future might have just vanished. In utter despair, I vented to my bridesmaid's group chat for some sympathetic words from the girls. Instead, one casually threw out a name that would change everything in a second: "Have you ever heard of Tech Cyber Force Recovery? They recovered Bitcoin for my cousin. You should call them." I had never heard of them before, but at that moment, I would have tried anything. I immediately looked them up, scoured reviews, and found story after story of people just like me—people who thought they had lost everything, only for Tech Cyber Force Recovery to pull off the impossible. That was all the convincing I needed. From the very first call, I knew I was in good hands. Their team was calm, professional, and incredibly knowledgeable. They explained the recovery process in a way that made sense, even through my stress-fogged brain. Every step of the way, they kept me informed, reassured me, and made me feel like this nightmare actually had a solution. And then, just a few days later, I got the message: "We have recovered your Bitcoin." (EMAIL. support @ tech cyber force recovery . com) OR WHATSAPP (+1 56 17 26 36 97) I could hardly believe my eyes: Six. Hundred. Thousand. Dollars. In my hands again. I let out my longest breath ever and almost cried, relieved. It felt like I woke up from a bad dream, but it was real, and Tech Cyber Force Recovery had done it. Because of them, I walked down the aisle not just as a bride, but as someone who had dodged financial catastrophe. Instead of spending my honeymoon stressing over lost funds, I got to actually enjoy it—knowing that my wallet, and my future, were secure. Would I refer to them? In a heartbeat. If you ever find yourself in that situation, please don't freak out, just call Tech Cyber Force Recovery. They really are the real deal.

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  • 07.03.25 15:18 Ariduk

    BEST LINK FOR RECOVERY SCAM ON TRADING INVESTMENT AND OTHERS TROUBLESHOOT?  Welcome To General Hacking Techniques Service known as DHACKERS.  Year 2025 we are active and best in what we do, as we give Solution to every problem concerning Web3 INTERNET activities, we guide you right to a positive fund Recovery e.t.c. Question From Most of Our Client, HOW POSSIBLE AND TIME WILL IT TAKE TO RECOVERY LOST OR SCAM  FUND? Our Answer.  Yes is 89.9% possible and how long it takes your Fund to be recovered depend on you.  The fact is there are lot of fake binary investment companies out their, same a lot fake recovery companies and agents too.                   CAUTION 1). Make sure you ask one or two questions concerning the service and how they render there recovery services. 2) Do not give out your scammed details to any agent or hacker when you are not yet ready to recover back your fund. 3) do not make any payment when you are not sure of the service if you are to make one. (4) We discovered that most of this fake hacker or agent do give us scam details, playing the victim, because they can afford the service charge. 5) As long you have your scam details with you, you can recover your fund back anytime any-day with the right channel. Contact us from our Front Desk. [email protected] For advice and services, our standby Guru email. [email protected] [email protected]  List of Service. ▶️Binary Recovery ▶️Data Recovery  ▶️University Result Upgraded ▶️Clear your Internet Blunder and controversy  ➡️Increase your Credit Score  ➡️Wiping of Criminal Records  ➡️Social Media Hack  ➡️Blank ATM Card  ➡️Load and wipe ➡️Phone Hacking ➡️Private Key Reset etc.  For quick response. Email [email protected]  Border us with your jobs and allow us give you positive result with our hacking skills.  All Right Reserved (c)2025

  • 08.03.25 04:42 andreassenhedda

    If you’ve fallen victim to online scammers who have deceived you into investing your hard-earned money through fraudulent Bitcoin schemes, know that you're not alone. Countless individuals have been misled by scammers using various tactics to swindle money through deceptive investment platforms or promises of high returns. These scams often come in the form of fake cryptocurrency investments, Ponzi schemes, or phishing scams designed to steal your Bitcoin and other assets. Unfortunately, many victims suffer not only financial loss but also the psychological toll of realizing they’ve been taken advantage of. In some cases, scammers go even further, threatening legal consequences or using intimidation to keep victims silent. However, all hope is not lost. There are ways to recover your funds and potentially even bring those responsible to justice. One promising resource that can assist in reclaiming lost funds is Lee Ultimate Hacker, a trusted platform designed to help victims of online fraud. This service specializes in helping individuals who have been scammed through cryptocurrency investments or similar schemes. The platform’s expertise can help you navigate the process of recovering your money, giving you a chance to fight back against those who’ve wronged you. To begin the recovery process, it’s important to gather any proof of payment or transaction history involving the scammers. This evidence is crucial in tracing the flow of funds and identifying the scam operation behind it. Once you have collected your documentation, you can reach out to Lee Ultimate Hacker via LEEULTIMATEHACKER @ AOL . COM or wh@tsapp +1 (715) 314 - 9248 for assistance. They offer professional services to investigate fraudulent schemes, track Bitcoin transactions, and work to reverse fraudulent transfers. The recovery process can be complex and requires expert knowledge of blockchain technology and financial investigations, but with the help of a dedicated team, you’ll have a better chance of seeing your funds returned. In addition to helping you recover your assets, Lee Ultimate Hacker also works towards identifying the scammers and reporting them to the appropriate authorities. They understand the urgency of halting these fraudsters before they can victimize others. With their assistance, you not only increase the chances of recovering your funds but also play a part in holding cybercriminals accountable. If you've been affected by Bitcoin scams or other fraudulent online activities, reaching out to a service like Lee Ultimate Hacker can provide hope and a clear path forward. Their team can guide you through the recovery process, offering expert support while you take steps to reclaim what you’ve lost. It’s time to take action and work toward reclaiming your hard-earned money.

  • 08.03.25 18:20 Diegolola514gmail.com

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  • 10.03.25 18:18 springwilli

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  • 12.03.25 05:35 cholevasaca

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  • 17.03.25 15:23 Charlesagrimes

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