@inproceedings{
moskovitz2023confronting,
title={Confronting Reward Model Overoptimization with Constrained {RLHF}},
author={Ted Moskovitz and Aaditya Singh and DJ Strouse and Tuomas Sandholm and Ruslan Salakhutdinov and Anca Dragan and Stephen McAleer},
booktitle={NeurIPS 2023 Foundation Models for Decision Making Workshop},
year={2023},
url={https://openreview.net/forum?id=0ByowhQGUG}
}
Large language models are typically aligned with human preferences by optimizing reward models (RMs) fitted to human feedback. However, human preferences are multi-faceted, and it is increasingly common to derive reward from a composition of simpler reward models which each capture a different aspect of language quality. This itself presents a challenge, as it is difficult to appropriately weight these component RMs when combining them. Compounding this difficulty, because any RM is only a proxy for human evaluation, this process is vulnerable to overoptimization, wherein past a certain point, accumulating higher reward is associated with worse human ratings. In this paper, we perform, to our knowledge, the first study on overoptimization in composite RMs, showing that correlation between component RMs has a significant effect on the locations of these points. We then introduce an approach to solve this issue using constrained reinforcement learning as a means of preventing the agent from exceeding each RM’s threshold of usefulness. Our method addresses the problem of weighting component RMs by learning dynamic weights, naturally given by the Lagrange multipliers. As a result, each RM stays within the range at which it is an effective proxy, improving evaluation performance. Finally, we introduce an adaptive method using gradient-free optimization to identify and optimize towards these points during a single run.
Confronting Reward Model Overoptimization with Constrained RLHF
Ted Moskovitz Aaditya Singh DJ Strouse Tuomas Sandholm Ruslan Salakhutdinov Anca Dragan Stephen McAleer