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huggingface avatar

huggingface/trl

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hf.co/docs/trl↗

Trl

This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities.

The project distinguishes itself through specialized alignment and optimization techniques, including direct preference optimization and reinforcement learning, which allow models to be tuned against human preferences without complex reward modeling. It further supports training efficiency through asynchronous rollout decoupling, which separates generation from gradient updates, and improves convergence stability by utilizing bias-corrected moving averages for model weights.

Beyond core training, the library includes utilities for knowledge distillation to transfer capabilities from large teacher models to smaller architectures. It also provides integrated tools for monitoring training progress, logging model completions, and tracking evaluation traces to support performance analysis throughout the development lifecycle.

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Features

  • Transformer Reinforcement Learning Libraries - Provides a framework for fine-tuning and aligning transformer language models using reinforcement learning and preference optimization.
  • Alignment Toolkits - Provides a comprehensive toolkit for optimizing language models to follow human preferences and improve reasoning capabilities.
  • Preference Optimization - Aligns language models with human preferences using direct preference optimization without requiring complex reward modeling.
  • Transformer Training Toolkits - Provides a library for training and fine-tuning transformer models on custom datasets with advanced optimization techniques.
  • Reward Modeling - Provides specialized reward model training to guide language model alignment with human preferences.
  • Language Model Fine-Tuning - Facilitates language model fine-tuning by training transformer models on specific datasets using supervised learning techniques.
  • Reinforcement Learning Alignment - Aligns language models with human preferences using reinforcement learning techniques to improve output quality and safety.
  • Supervised Fine-Tuning - Adapts transformer models to specific domains using supervised fine-tuning pipelines on curated datasets.
  • Knowledge Distillation - Transfers complex capabilities from large teacher models to smaller architectures to reduce computational requirements.
  • Asynchronous Rollout Decoupling - Implements asynchronous rollout decoupling to separate generation from gradient updates for improved training throughput.
  • Model Distillation Frameworks - Provides utilities for transferring knowledge from large teacher models to smaller student models to enhance deployment efficiency.
  • Reasoning Optimization - Provides model reasoning optimization by training models using a two-stage reinforcement learning approach.
  • Weight Smoothing - Implements bias-corrected moving averages to maintain smoothed model weights for improved training stability.
  • Reasoning Optimization - Improves complex reasoning performance through two-stage reinforcement learning and offline value function estimation.
  • On-Policy - Improves model reasoning performance through on-policy reinforcement learning and value function estimation.
  • Training Stability Techniques - Ensures training stability through bias-corrected moving averages to prevent divergence during optimization.
  • AI Observability and Evaluation - Tracks evaluation traces and model predictions to validate performance and reasoning quality.
  • Asynchronous Training - Supports asynchronous training by decoupling rollout generation from gradient updates to allow simultaneous processing.
  • Bias-Corrected Weight Averaging - Maintains bias-corrected moving averages of model weights to ensure training stability and faster convergence.
  • Large Language Models - Training transformer models with reinforcement learning.
  • Model Fine Tuning - Trains transformer models using reinforcement learning techniques.
  • Model Optimization and Deployment - Tools for training transformers using reinforcement learning.
  • Model Training - Reinforcement learning tools for training transformer language models.
  • Model Training and Fine-tuning - Official library for reinforcement learning and preference tuning.
  • Reinforcement Learning - Training transformer models with reinforcement learning.
  • Reinforcement Learning Frameworks - Standard library for transformer reinforcement learning.
  • Training and Fine-Tuning - Framework for training transformers with reinforcement learning.
  • Distribution Bias Correction - Adjusts training for divergent prompt distributions between datasets using embedding-based matching.
  • Training Progress Monitors - Displays real-time training and evaluation progress in the terminal using a rich, formatted interface.
  • Distribution Matching - Adjusts training objectives by aligning prompt distributions of preferred and rejected datasets to reduce inherent model bias.
  • Completion Loggers - Logs generated model completions to external platforms to support performance analysis.
18,653 stars·2,792 forks·Python·Apache-2.0·54 views

Star history

Star history chart for huggingface/trlStar history chart for huggingface/trl

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does huggingface/trl do?

This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities.

What are the main features of huggingface/trl?

The main features of huggingface/trl are: Transformer Reinforcement Learning Libraries, Alignment Toolkits, Preference Optimization, Transformer Training Toolkits, Reward Modeling, Language Model Fine-Tuning, Reinforcement Learning Alignment, Supervised Fine-Tuning.

Which projects share features with huggingface/trl?

Projects with overlapping indexed features include: openrlhf/openrlhf — OpenRLHF is a training framework and alignment library designed for reinforcement learning from human feedback across… verl-project/verl — This project is a distributed training infrastructure designed for aligning large language models through… axolotl-ai-cloud/axolotl — Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large… volcengine/verl — verl is a distributed training system designed for large language model alignment and reinforcement learning. It… oumi-ai/oumi — Oumi is a comprehensive large language model development platform designed for synthesizing data, fine-tuning models,… rlinf/rlinf — RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the…

Projects sharing features with Trl

These projects share indexed features with Trl. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • openrlhf/openrlhfOpenRLHF avatar

    OpenRLHF/OpenRLHF

    10,146View on GitHub↗

    OpenRLHF is a training framework and alignment library designed for reinforcement learning from human feedback across distributed GPU clusters. It provides tools for aligning large language models and multimodal vision-language models using algorithms such as PPO, GRPO, and DPO. The framework distinguishes itself through a distributed inference engine that overlaps sample rollout with training to increase throughput. It supports scaling to models exceeding 70 billion parameters via parameter sharding and handles long-context sequences through ring-attention sequence parallelism. The project

    Pythonlarge-language-modelsopenai-o1proximal-policy-optimization
    View on GitHub↗10,146
  • verl-project/verlverl-project avatar

    verl-project/verl

    24,163View on GitHub↗

    This project is a distributed training infrastructure designed for aligning large language models through reinforcement learning. It functions as an end-to-end engine for complex alignment tasks, including proximal policy optimization, direct preference optimization, and iterative self-play. By providing a unified framework for multi-turn interactions and tool-use scenarios, it enables the development of models capable of reasoning and external environment engagement. The framework distinguishes itself through a decoupled architecture that separates model training from sample generation. This

    Python
    View on GitHub↗24,163
  • axolotl-ai-cloud/axolotlaxolotl-ai-cloud avatar

    axolotl-ai-cloud/axolotl

    12,672View on GitHub↗

    Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies. The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation,

    Pythonfine-tuningllm
    View on GitHub↗12,672
  • volcengine/verlvolcengine avatar

    volcengine/verl

    22,015View on GitHub↗

    verl is a distributed training system designed for large language model alignment and reinforcement learning. It provides a framework for executing post-training pipelines, including supervised fine-tuning and reinforcement learning from human feedback, to refine model behavior and agentic capabilities. The system utilizes a hybrid training and inference engine that optimizes memory and communication when switching between model generation and gradient updates. It supports multi-modal reinforcement learning for models processing both image and text data, and implements algorithms such as PPO

    Python
    View on GitHub↗22,015
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