@inproceedings{wang-etal-2026-perm,
    title = "{PERM}: Psychology-grounded Empathetic Reward Modeling for Large Language Models",
    author = "Wang, Chengbing  and
      Zheng, Wuqiang  and
      Zhang, Yang  and
      Zhu, Fengbin  and
      Cheng, Junyi  and
      Xie, Yi  and
      Wang, Wenjie  and
      Feng, Fuli",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.363/",
    pages = "7350--7373",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL) has been utilized to enhance empathy of LLMs, existing reward models typically evaluate empathy from a single perspective, overlooking the inherently bidirectional interaction nature of empathy between the supporter and seeker as defined by Empathy Cycle theory. To address this limitation, we propose Psychology-grounded Empathetic Reward Modeling (PERM). PERM operationalizes empathy evaluation through a bidirectional decomposition: 1) Supporter perspective, assessing internal resonation and communicative expression; 2) Seeker perspective, evaluating emotional reception. Additionally, it incorporates a bystander perspective to monitor overall interaction quality. Extensive experiments on a widely-used emotional intelligence benchmark and an industrial daily conversation dataset demonstrate that PERM outperforms state-of-the-art baselines by over 10{\%}. Furthermore, a blinded user study reveals a 70{\%} preference for our approach, highlighting its efficacy in generating more empathetic responses."
}