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Arxiv
1d
42
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Active teacher selection for reinforcement learning from human feedback
- Reinforcement learning from human feedback (RLHF) enables machine learning systems to learn objectives from human feedback.
- The Hidden Utility Bandit (HUB) framework is proposed to model differences in teacher rationality, expertise, and costliness, formalizing the problem of learning from multiple teachers.
- The Active Teacher Selection (ATS) algorithm outperforms baseline algorithms by actively selecting when and which teacher to query.
- The HUB framework and ATS algorithm facilitate future research on active teacher selection for robust reward modeling.
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