TRL - Transformers 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.

🎉 What’s New

⚡ Up to 6.9× longer sequences on the same GPU: SFT, DPO, KTO, GRPO and RLOO now score tokens with a fused LM head by default. At 8k tokens that is 52% to 82% less peak VRAM.

Taxonomy

Below is an overview of TRL trainers, organized by maturity and method type.

Online methods

Reward modeling

Offline methods

Knowledge distillation

Experimental

Online methods

Offline methods

Knowledge distillation

You can also explore TRL-related models, datasets, and demos in the TRL Hugging Face organization.

Learn

Learn post-training with TRL and other libraries in 🤗 smol course.

Contents

The documentation is organized into the following sections:

Blog posts

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Published on May 27, 2026

Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL

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Published on March 31, 2026

TRL v1: Post-Training Library That Holds When the Field Invalidates Its Own Assumptions

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Published on March 10, 2026

Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries

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Published on March 9, 2026

Ulysses Sequence Parallelism: Training with Million-Token Contexts

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Published October 23, 2025

Building the Open Agent Ecosystem Together: Introducing OpenEnv

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Published on August 7, 2025

Vision Language Model Alignment in TRL ⚡️

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Published on June 3, 2025

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

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Published on May 25, 2025

🐯 Liger GRPO meets TRL

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Published on January 28, 2025

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

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Published on July 10, 2024

Preference Optimization for Vision Language Models with TRL

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Published on June 12, 2024

Putting RL back in RLHF

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Published on January 10, 2024

Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL

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Published on September 29, 2023

Finetune Stable Diffusion Models with DDPO via TRL

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Published on August 8, 2023

Fine-tune Llama 2 with DPO

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Published on April 5, 2023

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

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Published on March 9, 2023

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

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Published on December 9, 2022

Illustrating Reinforcement Learning from Human Feedback

Talks

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Talk given on October 30, 2025

Fine tuning with TRL

Update on GitHub