# TRL - Transformer Reinforcement Learning TRL is a full stack library where we provide a set of tools to train transformer language models with methods like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more. The library is integrated with 🤗 [transformers](https://github.com/huggingface/transformers). You can also explore TRL-related models, datasets, and demos in the [TRL Hugging Face organization](https://huggingface.co/trl-lib). ## Learn Learn post-training with TRL and other libraries in 🤗 [smol course](https://github.com/huggingface/smol-course). ## Contents The documentation is organized into the following sections: - **Getting Started**: installation and quickstart guide. - **Conceptual Guides**: dataset formats, training FAQ, and understanding logs. - **How-to Guides**: reducing memory usage, speeding up training, distributing training, etc. - **Integrations**: DeepSpeed, Liger Kernel, PEFT, etc. - **Examples**: example overview, community tutorials, etc. - **API**: trainers, utils, etc. ## Blog posts

Published on June 3, 2025

NO GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL

Published on May 25, 2025

🐯 Liger GRPO meets TRL

Published on January 28, 2025

Open-R1: a fully open reproduction of DeepSeek-R1

Published on July 10, 2024

Preference Optimization for Vision Language Models with TRL

Published on June 12, 2024

Putting RL back in RLHF

Published on September 29, 2023

Finetune Stable Diffusion Models with DDPO via TRL

Published on August 8, 2023

Fine-tune Llama 2 with DPO

Published on April 5, 2023

StackLLaMA: A hands-on guide to train LLaMA with RLHF

Published on March 9, 2023

Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU

Published on December 9, 2022

Illustrating Reinforcement Learning from Human Feedback