Greedy Sampling Is Provably Efficient for RLHF

arXiv — stat.ML•Wednesday, October 29, 2025 at 4:00:00 AM

A recent study highlights the efficiency of greedy sampling in Reinforcement Learning from Human Feedback (RLHF), a crucial method for enhancing large language models. While RLHF has shown great promise, understanding its theoretical foundations has been challenging. This research sheds light on the complexities of learning with preference feedback, particularly in relation to the Bradley-Terry model. By addressing these challenges, the findings could lead to more effective applications of RLHF, making it a significant step forward in the field of artificial intelligence.

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