  Despite the success of reinforcement learning from human feedback (RLHF) in
aligning language models with human values, reward hacking, also termed reward
overoptimization, remains a critical challenge. This issue primarily arises
from reward misgeneralization, where reward models (RMs) compute reward using
spurious features that are irrelevant to human preferences. In this work, we
tackle this problem from an information-theoretic perspective and propose a
framework for reward modeling, namely InfoRM, by introducing a variational
information bottleneck objective to filter out irrelevant information. Notably,
we further identify a correlation between overoptimization and outliers in the
IB latent space of InfoRM, establishing it as a promising tool for detecting
reward overoptimization. Inspired by this finding, we propose the Cluster
Separation Index (CSI), which quantifies deviations in the IB latent space, as
an indicator of reward overoptimization to facilitate the development of online
mitigation strategies. Extensive experiments on a wide range of settings and RM
scales (70M, 440M, 1.4B, and 7B) demonstrate the effectiveness of InfoRM.
Further analyses reveal that InfoRM's overoptimization detection mechanism is
not only effective but also robust across a broad range of datasets, signifying
a notable advancement in the field of RLHF. The code will be released upon
acceptance.
