Project Title

trl — Train transformer language models with reinforcement learning

Overview

trl is a comprehensive library designed for post-training foundation models using advanced techniques such as Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO). Built on top of the Hugging Face Transformers ecosystem, trl supports various model architectures and modalities, and can be scaled-up across different hardware setups.

Key Features

Trainers: Access various fine-tuning methods via trainers like SFTTrainer, GRPOTrainer, DPOTrainer, RewardTrainer, and more.
Efficient and scalable: Leverages Hugging Face Accelerate to scale from single GPU to multi-node clusters using methods like DDP and DeepSpeed.
Command Line Interface (CLI): A simple interface lets you fine-tune with models without needing to write code.

Use Cases

Researchers and developers looking to fine-tune transformer language models using reinforcement learning techniques.
Teams needing to scale up training across multiple hardware setups.
Individuals wanting to experiment with advanced fine-tuning methods without extensive coding.

Advantages

Supports a wide range of model architectures and modalities.
Integrates with Hugging Face's Transformers ecosystem for seamless model training and deployment.
Provides a simple CLI for easy fine-tuning without coding.

Limitations / Considerations

The project's license is currently unknown, which may affect its use in commercial applications.
As with any machine learning library, the effectiveness of trl will depend on the quality and size of the training data.

Hugging Face Transformers: A widely-used library for state-of-the-art natural language processing, on which trl is built. Transformers offers a broader range of NLP tasks beyond reinforcement learning.
Ray RLlib: An open-source library for reinforcement learning, which provides a different approach to training reinforcement learning models.
DeepMind's Acme: A library for reinforcement learning research, offering a research-focused approach compared to trl's focus on practical applications.

Basic Information

GitHub: https://github.com/huggingface/trl
Stars: 15,177
License: Unknown
Last Commit: 2025-08-20

📊 Project Information

Project Name: trl
GitHub URL: https://github.com/huggingface/trl
Programming Language: Python
⭐ Stars: 15,177
🍴 Forks: 2,127
📅 Created: 2020-03-27
🔄 Last Updated: 2025-08-20

🏷️ Project Topics

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📚 Documentation

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trl

Project Description