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🗞️ Update "What's New" (#4338)
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README.md

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## 🎉 What's New
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> **✨ 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:
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>
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> - [OpenAI Cookbook](https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers)
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> - [GPT OSS recipes](https://github.com/huggingface/gpt-oss-recipes)
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> - [Our example script](https://github.com/huggingface/trl/blob/main/examples/scripts/sft_gpt_oss.py)
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**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.
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Explore how to seamlessly integrate TRL with OpenEnv in our [dedicated documentation](openenv).
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## Overview
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docs/source/index.md

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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.
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The library is integrated with 🤗 [transformers](https://github.com/huggingface/transformers).
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Below is the current list of TRL trainers, organized by method type (⚡️ = vLLM support; 🧪 = experimental).
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## 🎉 What's New
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**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.
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Explore how to seamlessly integrate TRL with OpenEnv in our [dedicated documentation](openenv).
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## Taxonomy
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Below is the current list of TRL trainers, organized by method type (⚡️ = vLLM support; 🧪 = experimental).
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## 🎉 What's New
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**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.
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Explore how to seamlessly integrate TRL with OpenEnv in our [dedicated documentation](openenv).
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You can also explore TRL-related models, datasets, and demos in the [TRL Hugging Face organization](https://huggingface.co/trl-lib).
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## Learn

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