  We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the
pervasive issue of reward over-optimization in Reinforcement Learning from
Human Feedback (RLHF) for Large Language Models (LLMs). Over-optimization
occurs when a reward model serves as an imperfect proxy for human preference,
and RL-driven policy optimization erroneously exploits reward inaccuracies. In
this paper, we begin by introducing a lightweight way to quantify uncertainties
in rewards, relying solely on the last layer embeddings of the reward model,
without the need for computationally expensive reward ensembles. AdvPO then
addresses a distributionally robust optimization problem centred around the
confidence interval of the reward model's predictions for policy improvement.
Through comprehensive experiments on the Anthropic HH and TL;DR summarization
datasets, we illustrate the efficacy of AdvPO in mitigating the
overoptimization issue, consequently resulting in enhanced performance as
evaluated through human-assisted evaluation.
