Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration

• URL: http://arxiv.org/abs/2312.00267v1
• Date: Fri, 1 Dec 2023 00:54:02 GMT
• Title: Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration
• Authors: Viraj Mehta and Vikramjeet Das and Ojash Neopane and Yijia Dai and Ilija Bogunovic and Jeff Schneider and Willie Neiswanger
• Abstract summary: Preference-based feedback is important for many applications in reinforcement learning. In this work, we take advantage of the fact that one can often choose contexts to obtain human feedback. We show that our method is able to reach better performance with fewer samples of human preferences than multiple baselines.
• Score: 29.935758027209292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preference-based feedback is important for many applications in reinforcement learning where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback (RLHF) on large language models. For many applications of RLHF, the cost of acquiring the human feedback can be substantial. In this work, we take advantage of the fact that one can often choose contexts at which to obtain human feedback in order to most efficiently identify a good policy, and formalize this as an offline contextual dueling bandit problem. We give an upper-confidence-bound style algorithm for this problem and prove a polynomial worst-case regret bound. We then provide empirical confirmation in a synthetic setting that our approach outperforms existing methods. After, we extend the setting and methodology for practical use in RLHF training of large language models. Here, our method is able to reach better performance with fewer samples of human preferences than multiple baselines on three real-world datasets.

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