Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment
This addresses a key problem in aligning LLMs to human preferences by mitigating reward hacking, though it is an incremental improvement over existing methods.
The paper tackles reward hacking in Best-of-N sampling for language model alignment by proposing MBR-BoN, which incorporates a Minimum Bayes Risk objective as a proximity regularizer, and shows it outperforms baseline methods on datasets like AlpacaFarm and Anthropic's hh-rlhf, with models trained on MBR-BoN-generated data also outperforming those using vanilla BoN.
Best-of-N (BoN) sampling with a reward model has been shown to be an effective strategy for aligning Large Language Models (LLMs) to human preferences at the time of decoding. BoN sampling is susceptible to a problem known as reward hacking when the accuracy of the reward model is not high enough due to the quality or the quantity of the preference dataset. Because the reward model is an imperfect proxy for the true objective, over-optimizing its value can compromise its performance on the true objective. In this research, we propose MBR-BoN, a variant of BoN that aims to mitigate reward hacking at inference time by incorporating the Minimum Bayes Risk (MBR) objective as a proximity regularization term. We show empirically and analytically that the MBR objective quantifies the proximity of the response to the reference policy, serving as a proximity regularizer. We evaluate MBR-BoN on the AlpacaFarm and Anthropic's hh-rlhf datasets and show that it outperforms both BoN sampling and MBR decoding. We also evaluate MBR-BoN to generate a pairwise preference learning dataset for Direct Preference Optimization (DPO). Empirical results show that models trained on a dataset generated with MBR-BoN outperform those with vanilla BoN. Our code is available at https://github.com/CyberAgentAILab/regularized-bon