@inproceedings{liu-etal-2025-cogdual,
    title = "{C}og{D}ual: Enhancing Dual Cognition of {LLM}s via Reinforcement Learning with Implicit Rule-Based Rewards",
    author = "Liu, Cheng  and
      Lu, Yifei  and
      Ye, Fanghua  and
      Li, Jian  and
      Chen, Xingyu  and
      Ren, Feiliang  and
      Tu, Zhaopeng  and
      Li, Xiaolong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1389/",
    pages = "27295--27324",
    ISBN = "979-8-89176-332-6",
    abstract = "Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering or supervised fine-tuning to enable models to imitate character behaviors in specific scenarios, but often neglect the underlying cognitive mechanisms driving these behaviors. Inspired by cognitive psychology, we introduce CogDual, a novel RPLA adopting a cognize-then-respond reasoning paradigm. By jointly modeling external situational awareness and internal self-awareness, CogDual generates responses with improved character consistency and contextual alignment. To further optimize the performance, we employ reinforcement learning with two general-purpose reward schemes designed for open-domain text generation. Extensive experiments on the CoSER benchmark, as well as Cross-MR and LifeChoice, demonstrate that CogDual consistently outperforms existing baselines and generalizes effectively across diverse role-playing tasks."
}