Jackwang111/M2RL-RL_Science

<h1 align="center">To Mix or To Merge?<br/>Toward Multi-Domain Reinforcement Learning for Large Language Models</h1>

<p align="center"> <a href="https://arxiv.org/abs/2602.12566"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg" alt="arXiv"></a> <a href="https://hf.co/collections/Jackwang111/m2rl"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-ffd21e.svg" alt="Hugging Face"></a> <a href="https://modelscope.cn/collections/whq1111/M2RL"><img src="https://img.shields.io/badge/ModelScope-Models-5f4bff.svg" alt="ModelScope"></a> <a href="https://github.com/Mosi-AI/M2RL"><img src="https://img.shields.io/badge/GitHub-Code-181717.svg?logo=github" alt="GitHub"></a> <a href="https://openreview.net/forum?id=jP7j5XkG8J"><img src="https://img.shields.io/badge/COLM-2026-4b8bbe.svg" alt="COLM 2026"></a> </p>

<p align="center"> <b>Haoqing Wang^†, Xiang Long^†, Ziheng Li^†, Yilong Xu, Tingguang Li, Yehui Tang^✉</b><br/> Samsung Research, Beijing, China &nbsp;&nbsp;·&nbsp;&nbsp; Peking University </p>

📰 News

• —[2026.09.07] 🎉 The model checkpoints are now open-sourced on Hugging Face and ModelScope! Feel free to use our checkpoints for your post-training research (e.g., weight merging, multi-teacher on-policy distillation)!
• —[2026.07.09] 🎉 Our paper is accepted to COLM 2026!

📚 Citation

If you find this work useful, please consider citing:

@inproceedings{
wang2026to,
title={To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models},
author={Haoqing Wang and Xiang Long and Ziheng Li and Yilong Xu and Tingguang Li and Yehui Tang},
booktitle={Third Conference on Language Modeling},
year={2026},
url={https://openreview.net/forum?id=jP7j5XkG8J}
}