  Reinforcement Learning (RL) from Human Preference-based feedback is a popular
paradigm for fine-tuning generative models, which has produced impressive
models such as GPT-4 and Claude3 Opus. This framework often consists of two
steps: learning a reward model from an offline preference dataset followed by
running online RL to optimize the learned reward model. In this work,
leveraging the idea of reset, we propose a new RLHF algorithm with provable
guarantees. Motivated by the fact that offline preference dataset provides
informative states (i.e., data that is preferred by the labelers), our new
algorithm, Dataset Reset Policy Optimization (DR-PO), integrates the existing
offline preference dataset into the online policy training procedure via
dataset reset: it directly resets the policy optimizer to the states in the
offline dataset, instead of always starting from the initial state
distribution. In theory, we show that DR-PO learns to perform at least as good
as any policy that is covered by the offline dataset under general function
approximation with finite sample complexity. In experiments, we demonstrate
that on both the TL;DR summarization and the Anthropic Helpful Harmful (HH)
dataset, the generation from DR-PO is better than that from Proximal Policy
Optimization (PPO) and Direction Preference Optimization (DPO), under the
metric of GPT4 win-rate. Code for this work can be found at
https://github.com/Cornell-RL/drpo.
