  Reinforcement learning from human feedback (RLHF) emerges as a promising
paradigm for aligning large language models (LLMs). However, a notable
challenge in RLHF is overoptimization, where beyond a certain threshold, the
pursuit of higher rewards leads to a decline in human preferences. In this
paper, we observe the weakness of KL regularization which is commonly employed
in existing RLHF methods to address overoptimization. To mitigate this
limitation, we scrutinize the RLHF objective in the offline dataset and propose
uncertainty-penalized RLHF (UP-RLHF), which incorporates uncertainty
regularization during RL-finetuning. To enhance the uncertainty quantification
abilities for reward models, we first propose a diverse low-rank adaptation
(LoRA) ensemble by maximizing the nuclear norm of LoRA matrix concatenations.
Then we optimize policy models utilizing penalized rewards, determined by both
rewards and uncertainties provided by the diverse reward LoRA ensembles. Our
experimental results, based on two real human preference datasets, showcase the
effectiveness of diverse reward LoRA ensembles in quantifying reward
uncertainty. Additionally, uncertainty regularization in UP-RLHF proves to be
pivotal in mitigating overoptimization, thereby contributing to the overall
performance.
