Subspace Inference Enables Efficient Active Reward Learning from Preferences
Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learning a critical component in synthesizing informative preference queries. However, effective uncertainty quantification required for active lea...
Yu-Tai Zhou, Erdem Biyik
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