@inproceedings{du-etal-2026-human,
    title = "{HER}: Human-like Reasoning and Reinforcement Learning for {LLM} Role-playing",
    author = "Du, Chengyu  and
      Wang, Xintao  and
      Chen, Aili  and
      Li, Weiyuan  and
      Xu, Rui  and
      Liu, Junteng  and
      Huang, Zishan  and
      Tian, Rong  and
      Sun, Zijun  and
      Li, Yuhao  and
      Feng, Liheng  and
      Ding, Deming  and
      Zhao, Pengyu  and
      Xiao, Yanghua",
    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.1283/",
    pages = "25725--25762",
    ISBN = "979-8-89176-395-1",
    abstract = "LLM role-playing, i.e., using large language models (LLMs) to simulate specific personas, has emerged as a key capability in various applications, such as companionship, content creation, and digital games. While current models effectively capture character tones and knowledge, simulating the inner thoughts behind their behaviors remains a non-trivial challenge. Towards cognitive simulation in LLM role-play, previous efforts have mainly suffered from two critical deficiencies: the lack of high-quality datasets with explicit reasoning traces and the absence of reliable reward signals aligned with human preferences. In this paper, we propose HER (Human Emulation Reasoning), a unified framework for cognitive-level persona simulation. HER introduces a dual-layer thinking mechanism that strictly distinguishes characters' first-person thinking processes from LLMs' third-person reasoning. To bridge the aforementioned gaps, we curate a reasoning-augmented role-playing dataset via a reverse engineering strategy for supervised learning, and construct human-aligned evaluation principles and preference-based reward models for role-play reinforcement learning. Leveraging these resources, we train HER models based on the Qwen3-32B backbone via a hybrid paradigm of supervised learning (SL) and reinforcement learning from human feedback (RLHF). Extensive experiments validate the effectiveness of our approach. Notably, our models significantly outperform the Qwen3-32B baseline, achieving a 30.26{\%} on the CoSER benchmark and a 14.97{\%} on the MiniMax Benchmark. Our datasets, evaluation principles, and trained models will be released to facilitate future research in cognitive-level LLM role-playing."
}