Text Classification
Transformers
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@@ -23,8 +23,8 @@ This repository provides trained checkpoints for reward modeling and user-level
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  ## Links
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- - 📄 **arXiv Paper**: https://arxiv.org/abs/XXXX.XXXXX
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- - 🤗 **Hugging Face Paper**: https://huggingface.co/papers/XXXX.XXXXX
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  - 💻 **GitHub Code**: https://github.com/ModalityDance/MRM
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  - 📦 **Hugging Face Collection**: https://huggingface.co/collections/ModalityDance/mrm
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@@ -156,11 +156,14 @@ print("reward(rejected)=", s_rj.tolist())
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  If you use this model or code in your research, please cite:
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  ```bibtex
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- @article{mrm2025,
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- title = {Meta Reward Modeling for Personalized Alignment},
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- author = {Author Names},
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- journal = {arXiv preprint arXiv:XXXX.XXXXX},
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- year = {2025}
 
 
 
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  }
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  ```
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  ## Links
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+ - 📄 **arXiv Paper**: https://arxiv.org/abs/2601.18731
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+ - 🤗 **Hugging Face Paper**: https://huggingface.co/papers/2601.18731
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  - 💻 **GitHub Code**: https://github.com/ModalityDance/MRM
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  - 📦 **Hugging Face Collection**: https://huggingface.co/collections/ModalityDance/mrm
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  If you use this model or code in your research, please cite:
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  ```bibtex
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+ @misc{cai2026adaptsanymetareward,
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+ title={One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment},
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+ author={Hongru Cai and Yongqi Li and Tiezheng Yu and Fengbin Zhu and Wenjie Wang and Fuli Feng and Wenjie Li},
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+ year={2026},
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+ eprint={2601.18731},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2601.18731},
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  }
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  ```
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