M2RL
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The SFT+RL+OPD model weights of paper "To mix or to merge: Toward multi-domain reinforcement learning for large language models" • 8 items • Updated • 1
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To Mix or To Merge?
Toward Multi-Domain Reinforcement Learning for Large Language Models
Haoqing Wang†, Xiang Long†, Ziheng Li†, Yilong Xu, Tingguang Li, Yehui Tang✉
Samsung Research, Beijing, China · Peking University
📰 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}
}
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