CRPO: Character-centric Group Relative Policy Optimization for Role-aware Reasoning in Role-playing Agents
arXiv:2605.25511v1 Announce Type: new Abstract: Recent advancements in Reinforcement Learning (RL), particularly Group Relative Policy Optimization (GRPO), have significantly enhanced the reasoning capabilities of Large Language Models. However, applying these problem-centric optimization methods to role-playing agents often leads to a loss of character fidelity and style collapse, as they prioritize context-specific utility over persona alignment. To address this, we propose Character-Centric Group Relative Policy Optimization (CRPO), a framework designed to realign RL objectives with the rol
The proliferation of advanced LLMs and their application in sophisticated, multi-agent environments increasingly highlights the limitations of 'problem-centric' optimization in maintaining persona consistency.
This development addresses a critical challenge in AI agent development, moving beyond task efficiency to ensure character fidelity, which is essential for robust and trustworthy autonomous systems.
The shift to character-centric optimization allows AI agents to maintain consistent personas during complex interactions, opening new possibilities for reliable role-playing and human-AI collaboration.
- · AI Agent Developers
- · Gaming & Entertainment Industry
- · Customer Service Automation
- · Virtual Companions
- · Developers of generic, un-personalized AI agents
- · Brands reliant on inconsistent AI personas
More sophisticated and believable AI agents become possible, enhancing user experience and application breadth.
The improved reliability of AI personas could accelerate the adoption of AI agents in sensitive or highly interactive roles.
This could lead to new ethical considerations around AI 'identity' and the depth of human-AI relationships.
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Read at arXiv cs.CL