Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

ArXi:2604.07343v1 Announce Type: cross Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values. While benchmarks for general response quality are prevalent, evaluating how well reward models account for individual user preferences remains an open challenge. To bridge this gap, we