39 lines
1.9 KiB
Markdown
39 lines
1.9 KiB
Markdown
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<h1 align="center">To Mix or To Merge?<br/>Toward Multi-Domain Reinforcement Learning for Large Language Models</h1>
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<p align="center">
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<a href="https://arxiv.org/abs/2602.12566"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg" alt="arXiv"></a>
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<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>
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<a href="https://modelscope.cn/collections/whq1111/M2RL"><img src="https://img.shields.io/badge/ModelScope-Models-5f4bff.svg" alt="ModelScope"></a>
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<a href="https://github.com/Mosi-AI/M2RL"><img src="https://img.shields.io/badge/GitHub-Code-181717.svg?logo=github" alt="GitHub"></a>
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<a href="https://openreview.net/forum?id=jP7j5XkG8J"><img src="https://img.shields.io/badge/COLM-2026-4b8bbe.svg" alt="COLM 2026"></a>
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</p>
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<p align="center">
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<b>Haoqing Wang<sup>†</sup>, Xiang Long<sup>†</sup>, Ziheng Li<sup>†</sup>, Yilong Xu, Tingguang Li, Yehui Tang<sup>✉</sup></b><br/>
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Samsung Research, Beijing, China · Peking University
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</p>
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---
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## 📰 News
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- **[2026.09.07]** 🎉 The model checkpoints are now open-sourced on [Hugging Face](https://hf.co/collections/Jackwang111/m2rl) and [ModelScope](https://modelscope.cn/collections/whq1111/M2RL)! **Feel free to use our checkpoints for your post-training research (e.g., weight merging, multi-teacher on-policy distillation)!**
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- **[2026.07.09]** 🎉 Our paper is accepted to **COLM 2026**!
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---
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## 📚 Citation
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If you find this work useful, please consider citing:
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```bibtex
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@inproceedings{
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wang2026to,
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title={To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models},
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author={Haoqing Wang and Xiang Long and Ziheng Li and Yilong Xu and Tingguang Li and Yehui Tang},
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booktitle={Third Conference on Language Modeling},
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year={2026},
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url={https://openreview.net/forum?id=jP7j5XkG8J}
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}
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```
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