Towards Reward Fairness in RLHF: From a Resource Allocation Perspective

Sheng Ouyang, Yulan Hu, Ge Chen +3

Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback (RLHF). However, if these rewards are inherently imperfect, exh…