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arXiv · 2605.29256

DynSess: Dynamic Session-Level Evaluation and Optimization Framework for Role-Playing Agents

Rongsheng Zhang·Jiji Tang·Junnan Ren·Zuyi Bao·Weijie Chen·Ruofan Hu·Zhou Zhao·Tangjie Lv·Yan Zhang
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Abstract

Role-playing with large language models is fundamentally a session-level task, requiring agents to sustain character identity and interaction quality across extended multi-turn conversations. Yet existing evaluation and optimization methods remain largely turn-level, failing to capture long-horizon quality. We propose DynSess, a unified session-level framework for role-playing agents. DynSess-Eval scores complete dialogue sessions via rubrics targeting long-horizon behaviors. Leveraging its session-level rewards, we construct high-quality training trajectories through multi-turn lookahead search and train DynSess-Character with two complementary variants: DSPO (off-policy) and GSRPO (on-policy). Experiments show that DynSess-Eval aligns with human judgments substantially better than prior evaluators, and blind human evaluation further shows that DynSess-Character matches the strongest character model despite using substantially fewer parameters, while maintaining strong role consistency and interactive ability. Our dataset and code will be released to facilitate future research.

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Rongsheng Zhang, Jiji Tang, Junnan Ren, Zuyi Bao, Weijie Chen, Ruofan Hu, Zhou Zhao, Tangjie Lv, Yan Zhang. 2026-05-28. DynSess: Dynamic Session-Level Evaluation and Optimization Framework for Role-Playing Agents. https://arxiv.org/abs/2605.29256

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