Evaluating the Effectiveness of Reward Modeling of Generative AI Systems
New research evaluating the effectiveness of reward modeling during Reinforcement Learning from Human Feedback (RLHF): “ SEAL: Systematic Error Analysis for Value ALignment.” The paper introduces quantitative metrics for evaluating the effectiveness of modeling and aligning human values: Abstract : Reinforcement Learning from Human Feedback (RLHF) aims to align language models (LMs) with human values by training reward models (RMs) on binary preferences and using these RMs to fine-tune the base LMs. Despite its importance, the internal mechanisms of RLHF remain poorly understood. This paper introduces new metrics to evaluate the effectiveness of modeling and aligning human values, namely feature [...]