P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist

• URL: http://arxiv.org/abs/2601.02986v1
• Date: Tue, 06 Jan 2026 12:53:53 GMT
• Title: P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist
• Authors: Kwangwook Seo, Dongha Lee,
• Abstract summary: We propose P-Check, a novel personalized reward modeling framework.<n>P-Check trains a plug-and-play checklist generator that synthesizes dynamic evaluation criteria for guiding the reward prediction.<n>We conduct experiments and demonstrate that P-Check not only improves reward accuracy but also enhances downstream personalized generation.
• Score: 11.399221632873934
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with individual preferences. However, existing approaches largely treat user context as a static or implicit conditioning signal, failing to capture the dynamic and multi-faceted nature of human judgment. In this paper, we propose P-Check, a novel personalized reward modeling framework, designed to train a plug-and-play checklist generator that synthesizes dynamic evaluation criteria for guiding the reward prediction. To better align these checklists with personalized nuances, we introduce Preference-Contrastive Criterion Weighting, a training strategy that assigns saliency scores to criteria based on their discriminative power for personalized judgment. We conduct extensive experiments and demonstrate that P-Check not only improves reward accuracy but also enhances downstream personalized generation, and remains robust in OOD scenarios.

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