Bayesian Reward Models for LLM Alignment
This addresses a key problem in ensuring LLM safety and alignment for AI developers, though it is incremental as it builds on existing reward modeling methods.
The paper tackles reward overoptimization in LLM alignment by proposing a Bayesian reward model that provides uncertainty estimates, which effectively mitigates the issue in best-of-n sampling.
To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used. LLM responses with high rewards are then selected through best-of-$n$ (BoN) sampling or the LLM is further optimized to produce responses with high rewards through reinforcement learning from human feedback (RLHF). However, these processes are susceptible to reward overoptimization or `hacking', where responses receive high rewards due to imperfections in the reward model rather than true preference, particularly as prompts or responses deviate from the training data. To address these challenges, we propose to train a Bayesian reward model, which signals higher uncertainty further from the training data distribution. We trained Bayesian reward models using Laplace approximation on LoRA weights, and found that the resulting uncertainty estimates can effectively mitigate reward overoptimization in BoN sampling.