{"ID":2891147,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17147","arxiv_id":"2507.17147","title":"CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards","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 \\emph{cognitive} mechanisms driving these behaviors. Inspired by cognitive psychology, we introduce \\textbf{CogDual}, a novel RPLA adopting a \\textit{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.","short_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 \\emph{cogn...","url_abs":"https://arxiv.org/abs/2507.17147","url_pdf":"https://arxiv.org/pdf/2507.17147v1","authors":"[\"Cheng Liu\",\"Yifei Lu\",\"Fanghua Ye\",\"Jian Li\",\"Xingyu Chen\",\"Feiliang Ren\",\"Zhaopeng Tu\",\"Xiaolong Li\"]","published":"2025-07-23T02:26:33Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\",\"Language Model\"]","has_code":false}