@inproceedings{liu-etal-2025-haf,
    title = "{HAF}-{RM}: A Hybrid Alignment Framework for Reward Model Training",
    author = "Liu, Shujun  and
      Shen, Xiaoyu  and
      Lai, Yuhang  and
      Wang, Siyuan  and
      Yue, Shengbin  and
      Huang, Zengfeng  and
      Huang, Xuanjing  and
      Wei, Zhongyu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.924/",
    pages = "18874--18893",
    ISBN = "979-8-89176-251-0",
    abstract = "The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards.In this paper, we propose a hybrid alignment framework **HAF-RM** for reward model training by introducing an additional constraint on token-level policy probabilities in addition to the reward score. It can simultaneously supervise the internal preference model at the token level and optimize the mapping layer of the reward model at the sequence level.Experiment results on five datasets sufficiently show the validity and effectiveness of our proposed hybrid framework for training a high-quality reward model.By decoupling the reward modeling procedure and incorporating hybrid supervision, our **HAF-RM** framework offers a principled and effective approach to enhancing the performance and alignment of reward models, a critical component in the responsible development of powerful language models. We release our code at [https://haf-rm.github.io](https://haf-rm.github.io)."
}