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    <abstract>Maintaining persona consistency across multi-turn dialogues remains a core challenge for role-playing language models. Off-policy distillation from external teachers incurs distribution mismatch that compounds across dialogue turns, while reinforcement learning struggles with reward ambiguity inherent in subjective persona fidelity. We propose OSPD, an on-policy self-distillation framework where the same model serves as both teacher and student under asymmetric information: the teacher receives a complete character profile while the student sees only a brief summary, and the student generates trajectories from its own policy. We find that teacher confidence in role-playing dialogue exhibits a bimodal structure—sharply peaked at character-critical tokens yet diffuse at generic utterances—and introduce role-aware divergence switching to match this structure. A progressive trait masking curriculum further forces staged internalization of character knowledge along semantic dimensions. Experiments on CharacterBench, CharacterEval, and SocialBench show that OSPD substantially improves persona consistency over supervised fine-tuning and multi-turn RL baselines, without requiring any external teacher or reward model.</abstract>
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%0 Conference Proceedings
%T OSPD: On-Policy Self-Distillation for Persona-Consistent Dialogue
%A Xu, Rui
%A Zhang, Yikai
%A Chen, Aili
%A Zhao, Zicheng
%A Yinghui, Xu
%A Wu, Libo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xu-etal-2026-ospd
%X Maintaining persona consistency across multi-turn dialogues remains a core challenge for role-playing language models. Off-policy distillation from external teachers incurs distribution mismatch that compounds across dialogue turns, while reinforcement learning struggles with reward ambiguity inherent in subjective persona fidelity. We propose OSPD, an on-policy self-distillation framework where the same model serves as both teacher and student under asymmetric information: the teacher receives a complete character profile while the student sees only a brief summary, and the student generates trajectories from its own policy. We find that teacher confidence in role-playing dialogue exhibits a bimodal structure—sharply peaked at character-critical tokens yet diffuse at generic utterances—and introduce role-aware divergence switching to match this structure. A progressive trait masking curriculum further forces staged internalization of character knowledge along semantic dimensions. Experiments on CharacterBench, CharacterEval, and SocialBench show that OSPD substantially improves persona consistency over supervised fine-tuning and multi-turn RL baselines, without requiring any external teacher or reward model.
%U https://aclanthology.org/2026.emnlp-main.2105/
%P 47912-47933

Markdown (Informal)

[OSPD: On-Policy Self-Distillation for Persona-Consistent Dialogue](https://aclanthology.org/2026.emnlp-main.2105/) (Xu et al., EMNLP 2026)

• OSPD: On-Policy Self-Distillation for Persona-Consistent Dialogue (Xu et al., EMNLP 2026)

ACL

• Rui Xu, Yikai Zhang, Aili Chen, Zicheng Zhao, Xu Yinghui, and Libo Wu. 2026. OSPD: On-Policy Self-Distillation for Persona-Consistent Dialogue. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47912–47933, Budapest, Hungary. Association for Computational Linguistics.