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Oct 24, 2025
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🗞️ Update "What's New" #4338

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8 changes: 3 additions & 5 deletions README.md
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## 🎉 What's New

> **✨ OpenAI GPT OSS Support**: TRL now fully supports fine-tuning the latest [OpenAI GPT OSS models](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4)! Check out the:
>
> - [OpenAI Cookbook](https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers)
> - [GPT OSS recipes](https://github.com/huggingface/gpt-oss-recipes)
> - [Our example script](https://github.com/huggingface/trl/blob/main/examples/scripts/sft_gpt_oss.py)
**OpenEnv Integration:** TRL now supports **[OpenEnv](https://huggingface.co/blog/openenv)**, the open-source framework from Meta for defining, deploying, and interacting with environments in reinforcement learning and agentic workflows.

Explore how to seamlessly integrate TRL with OpenEnv in our [dedicated documentation](openenv).

## Overview

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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).

Below is the current list of TRL trainers, organized by method type (⚡️ = vLLM support; 🧪 = experimental).
## 🎉 What's New

**OpenEnv Integration:** TRL now supports **[OpenEnv](https://huggingface.co/blog/openenv)**, the open-source framework from Meta for defining, deploying, and interacting with environments in reinforcement learning and agentic workflows.

Explore how to seamlessly integrate TRL with OpenEnv in our [dedicated documentation](openenv).

## Taxonomy

Below is the current list of TRL trainers, organized by method type (⚡️ = vLLM support; 🧪 = experimental).

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## 🎉 What's New

**OpenEnv Integration:** TRL now supports **[OpenEnv](https://huggingface.co/blog/openenv)**, the open-source framework from Meta for defining, deploying, and interacting with environments in reinforcement learning and agentic workflows.

Explore how to seamlessly integrate TRL with OpenEnv in our [dedicated documentation](openenv).

------------------

You can also explore TRL-related models, datasets, and demos in the [TRL Hugging Face organization](https://huggingface.co/trl-lib).

## Learn
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