CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling

Dengcan Liu, Fengkai Yang, Xiaohan Wang +7

Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on…