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

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

URL: http://arxiv.org/abs/2312.09244v3
Date: Fri, 16 Aug 2024 23:59:29 GMT
Title: Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
Authors: Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D'Amour, DJ Dvijotham, Adam Fisch, Katherine Heller, Stephen Pfohl, Deepak Ramachandran, Peter Shaw, Jonathan Berant,
Abstract summary: Reward models play a key role in aligning language model applications towards human preferences. A natural mitigation is to train an ensemble of reward models, aggregating over model outputs to obtain a more robust reward estimate. We show that reward ensembles do not eliminate reward hacking because all reward models in the ensemble exhibit similar error patterns.
Score: 62.146953368613815
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.