Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

TL;DR

This paper investigates the use of reward model ensembles to mitigate reward hacking in language model alignment, finding they help but do not fully eliminate the problem due to reward model underspecification.

Contribution

It demonstrates that reward ensembles reduce overoptimization and improve robustness, but underspecification still allows reward hacking phenomena to persist.

Findings

Reward models are underspecified and can give different rewards under distribution shift.

Reward ensembles mitigate overoptimization and improve generalization.

Ensembles do not fully eliminate reward hacking phenomena.

Abstract

Reward models play a key role in aligning language model applications towards human preferences. However, this setup creates an incentive for the language model to exploit errors in the reward model to achieve high estimated reward, a phenomenon often termed \emph{reward hacking}. A natural mitigation is to train an ensemble of reward models, aggregating over model outputs to obtain a more robust reward estimate. We explore the application of reward ensembles to alignment at both training time (through reinforcement learning) and inference time (through reranking). First, we show that reward models are \emph{underspecified}: reward models that perform similarly in-distribution can yield very different rewards when used in alignment, due to distribution shift. Second, underspecification results in overoptimization, where alignment to one reward model does not improve reward as measured by another reward model trained on the same data. Third, overoptimization is mitigated by the use of reward ensembles, and ensembles that vary by their \emph{pretraining} seeds lead to better generalization than ensembles that differ only by their \emph{fine-tuning} seeds, with both outperforming individual reward models. However, even pretrain reward ensembles do not eliminate reward hacking: we show several qualitative reward hacking phenomena that are not mitigated by ensembling because all reward models in the ensemble exhibit similar error patterns.