Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models
Binghai Wang · Yantao Liu · Yuxuan Liu · Tianyi Tang · Shenzhi Wang · Chang Gao · Chujie Zheng · Yichang Zhang · Le Yu · Shixuan Liu · Tao Gui · Qi Zhang · Xuanjing Huang · Bowen Yu · Fei Huang · Junyang Lin
摘要
Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize Outcome Accuracy , which undermines their ability to generalize during RLHF. We introduce Rationale Consistency ,...