Role-playing agents (RPAs) must sustain persona fidelity, natural interaction, and scene progression across multi-turn trajectories. However, supervised fine-tuning, preference optimization, and coarse episode-level RL can over-reinforce safe stylized responses that appear in-character but are repetitive, low-information, or weakly grounded in the evolving scene. We propose LSH-RL, a Long-Short Horizon Reinforcement Learning framework for persona-level RPAs. LSH-RL trains a shared policy through dynamic multi-role self-play, combining short-horizon utterance/action rewards with long-horizon character-trajectory rewards. A gated trajectory-to-turn reward fusion mechanism prevents locally degenerate turns from receiving undeserved long-horizon credit, while a dialogue-action curriculum supports staged optimization. Experiments on PersonaArena and CharacterBox show that LSH-RL improves over SFT and DPO in both dialogue-only and full interaction settings. Further analyses show reduced repetition and stronger human preferences, suggesting a path toward more coherent and interaction-rich persona-level RPAs. The source code is available at https://github.com/Shiwenlong-source/LSH-RL.

Large language models (LLMs) increasingly serve as interactive social agents, yet their ability to maintain coherent and authentic persona-level role-playing remains limited, particularly in realistic social scenarios. Existing research predominantly focuses on character-level settings and relies on static evaluation formats, failing to capture the complexity of everyday social interactions. In this work, we present PersonaArena, a dynamic simulation framework for evaluating and improving persona-level role-playing in LLMs. PersonaArena leverages a large, filtered corpus of user-generated social content to construct a nuanced persona bank, and elicits multi-turn, context-rich interactions within simulated social environments. Our framework features a multi-agent debating judge for holistic and unbiased assessment. Through extensive experiments, we demonstrate that PersonaArena enables rigorous evaluation and enhancement of LLMs’ role-playing capabilities, advancing the development of more authentic and socially adept AI agents. Our codes and long appendix are available at https://anonymous.4open.science/r/PersonaArena-B323/.