CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards
This work addresses the challenge of enhancing cognitive realism in role-playing language agents for applications in interactive AI, though it is incremental in building on existing reinforcement learning methods.
The paper tackles the problem of role-playing language agents neglecting cognitive mechanisms by introducing CogDual, a model that uses a cognize-then-respond paradigm to improve character consistency and contextual alignment, achieving consistent outperformance over baselines on benchmarks like CoSER, Cross-MR, and LifeChoice.
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.