Year: 2026
Type: conference-paper
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 plugand-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.[CODE]
Authors: Kwangwook Seo, Dongha Lee
Institution: Yonsei University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166837472