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News/Subspace Inference Enables Efficient Active Reward Learning from Preferences
arxiv
PublishedSeptember 4, 2026 at 4:00 AM
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Subspace Inference Enables Efficient Active Reward Learning from Preferences

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arXiv:2609.04066v1 Announce Type: cross Abstract: 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.

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