Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance

Reward models (RMs) are crucial in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values. However, RM training data is commonly recognized as low-quality, always containing preference conflicts and inductive biases, such as response length or speaking style, which can easily lead to reward overfitting and hacking. A few recent RM debiasing methods either target merely a single specific type of preference bias or only address simple linear bias relations such as Pearson coefficients. To mitigate more complicated inductive bias of reward modeling, inspired by the information bottleneck, we introduce a novel information-theoretic debiasing method called Debiasing via Information optimization for RM (DIR). More specifically, our method trains RMs by maximizing the mutual information (MI) between preference prediction and input response pairs, while minimizing the MI between RM outputs and biased attributes of preference inputs. With the theoretical justification of information theory, DIR can handle different types of bias with more comprehensive non-linear correlations, enlarging its real-world application scenarios. In experiments, we verify the effectiveness of DIR with three types of inductive biases: response length, sycophancy, and format. Based on the numerical results, we discover that DIR can not only effectively diminish target inductive biases but also improve RLHF performances on various benchmarks with better generalization abilities.