Online Iterative Reinforcement Learning from Human Feedback with General Preference Model
<|reference_start|>Online Iterative Reinforcement Learning from Human Feedback with General Preference Model: We study Reinforcement Learning from Human Feedback (RLHF) under a general preference oracle. In particular, we do not assume that there exists a reward function and the preference signal is drawn from the Bradley-Terry model as most of the prior works do. We consider a standard mathematical formulation, the reverse-KL regularized minimax game between two LLMs for RLHF under general preference oracle. The learning objective of this formulation is to find a policy so that it is consistently preferred by the KL-regularized preference oracle over any competing LLMs. We show that this framework is strictly more general than the reward-based one, and propose sample-efficient algorithms for both the offline learning from a pre-collected preference dataset and online learning where we can query the preference oracle along the way of training. Empirical studies verify the effectiveness of the proposed framework.<|reference_end|>
@article{ye2024online,
    title={Online Iterative Reinforcement Learning from Human Feedback with General
  Preference Model},
    author={Chenlu Ye, Wei Xiong, Yuheng Zhang, Hanze Dong, Nan Jiang, Tong Zhang},
    journal={arXiv preprint arXiv:2402.07314},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07314},
    primaryClass={cs.LG stat.ML}
}