PersRM-R1: Enhance Personalized Reward Modeling with Reinforcement Learning

• URL: http://arxiv.org/abs/2508.14076v1
• Date: Tue, 12 Aug 2025 14:25:58 GMT
• Title: PersRM-R1: Enhance Personalized Reward Modeling with Reinforcement Learning
• Authors: Mengdi Li, Guanqiao Chen, Xufeng Zhao, Haochen Wen, Shu Yang, Di Wang,
• Abstract summary: PersRM-R1 is the first reasoning-based reward modeling framework specifically designed to identify and represent personal factors from only one or a few personal exemplars.<n>Our approach combines synthetic data generation with a two-stage training pipeline consisting of supervised fine-tuning followed by reinforcement fine-tuning.<n> Experimental results demonstrate that PersRM-R1 outperforms existing models of similar size and matches the performance of much larger models in both accuracy and generalizability.
• Score: 7.899605480166484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reward models (RMs), which are central to existing post-training methods, aim to align LLM outputs with human values by providing feedback signals during fine-tuning. However, existing RMs struggle to capture nuanced, user-specific preferences, especially under limited data and across diverse domains. Thus, we introduce PersRM-R1, the first reasoning-based reward modeling framework specifically designed to identify and represent personal factors from only one or a few personal exemplars. To address challenges including limited data availability and the requirement for robust generalization, our approach combines synthetic data generation with a two-stage training pipeline consisting of supervised fine-tuning followed by reinforcement fine-tuning. Experimental results demonstrate that PersRM-R1 outperforms existing models of similar size and matches the performance of much larger models in both accuracy and generalizability, paving the way for more effective personalized LLMs.

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