Hugging Face发布多环境强化学习训练指南
The ultimate guide to multi-harness RL
针对本地LLM微调中常见的多环境RL对齐痛点，给出了基于TRL和Harbor的具体工程解法，实操性强，适合做Agent或高级微调的同学参考。
Hi folks, it's Lewis here from the post-training team at Hugging Face. We've been exploring how to train open models in different coding harnesses and wrote up a looong guide on how we solved this using open source libraries like TRL and the Harbor framework for RL environments. We hope you find this interesting, especially since everyone nowadays has their own custom harness (e.g. Pi + extensions) and now there's a recipe on how to squeeze the best performance on them with whatever open model you use as your daily driver. Happy to hear any comments or feedback!
大家好，我是来自 Hugging Face 后训练团队的 Lewis。我们一直在探索如何在不同的编码框架中训练开源模型，并撰写了一份详尽指南，介绍我们如何使用 TRL 等开源库以及用于强化学习环境的 Harbor 框架来解决这一问题。希望大家觉得有趣，尤其是因为如今每个人都有自己的定制框架（例如 Pi + 扩展），而现在我们有了一套方法，教大家如何在使用任何日常开源模型时榨取最佳性能。欢迎留言评论或反馈！
Link to the guide: https://huggingface.co/spaces/FineEnvs/multi-harness-rl
指南链接：https://huggingface.co/spaces/FineEnvs/multi-harness-rl
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