[2604.08986] PerMix-RLVR: Preserving Persona Expressivity under Verifiable-Reward Alignment

Computer Science > Computation and Language arXiv:2604.08986 (cs) [Submitted on 10 Apr 2026] Title:PerMix-RLVR: Preserving Persona Expressivity under Verifiable-Reward Alignment Authors:Jihwan Oh, Soowon Oh, Murad Aghazada, Minchan Jeong, Sungnyun Kim, Se-Young Yun View a PDF of the paper titled PerMix-RLVR: Preserving Persona Expressivity under Verifiable-Reward Alignment, by Jihwan Oh and 5 other authors View PDF HTML (experimental) Abstract:Persona prompting has been widely adopted to steer large language models (LLMs) behavior and improve their instruction performance by assigning specific characters. However, identifying an optimal persona is time-consuming, and its impact on output quality remains poorly understood. Prior work has mainly addressed this issue at the prompt level via inference-time strategies, incurring additional computation. In this work, we avoid inference-time prompt search by tackling persona sensitivity during training, aiming to train models that adapt their behavior to diverse personas while preserving task performance. In particular, we find that reinforcement learning with verifiable rewards (RLVR) systematically reduces sensitivity to persona prompts, but also reveals an inherent trade-off of outcome-based optimization: while RLVR improves robustness on tasks with verifiable goals, it can also degrade persona expressivity when needed, e.g., in-character role-playing. To address this limitation, we propose PerMix-RLVR, a persona-mixed RLVR st...