Circuit-Aware Reward Training: A Mechanistic Framework for Longtail Robustness in RLHF
This addresses reward hacking and misalignment in RLHF for AI safety applications, representing a novel method for a known bottleneck.
The paper tackles systematic failures of RLHF reward models on longtail distributions by proposing a mechanistic interpretability framework that identifies specialized neural circuits for rare-event processing, and introduces Circuit-Aware Reward Training (CART) to improve longtail robustness through data augmentation, regularization, and ensemble strategies.
Reinforcement Learning from Human Feedback (RLHF) reward models exhibit systematic failures on longtail distributions, leading to reward hacking and misalignment. We propose a mechanistic interpretability framework that identifies specialized neural circuits responsible for rare-event processing in reward models. Drawing from recent advances showing distributed specialization for rare tokens in language models\citep{liu2025no, liu2025emergent}, we hypothesize that reward models also develop functionally distinct circuits for longtail scenarios. Our theoretical framework establishes formal connections between circuit specialization, reward generalization bounds, and longtail performance. We introduce \textbf{Circuit-Aware Reward Training (CART)}, which uses circuit analysis to guide data augmentation, regularization, and ensemble strategies. This approach provides both theoretical insights into reward model failures and practical interventions for improving longtail robustness.