From Demonstrations to Rewards: Alignment Without Explicit Human Preferences

ICLR 2025 Conference Submission12942 Authors

28 Sept 2024 (modified: 13 Oct 2024)ICLR 2025 Conference SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Keywords: inverse reinforcement learning, iterative RLHF, learning from demonstrations, reward learning
Abstract: One of the challenges of aligning large models with human preferences lies in both the data requirements and the technical complexities of current approaches. Predominant methods, such as RLHF, involve multiple steps, each demanding distinct types of data, including demonstrations data and preference data. In RLHF, human preferences are typically modeled through a reward model, which serves as a proxy to guide policy learning during the reinforcement learning stage, ultimately producing a policy aligned with human preferences. However, in this paper, we propose a fresh perspective on learning alignment based on inverse reinforcement learning principles, where the optimal policy is still derived from reward maximization. However, instead of relying on preference data, we directly learn the reward model from demonstration data. This new formulation offers the flexibility to be applied even when only demonstration data is available, a capability that current RLHF methods lack, and it also shows that demonstration data offers more utility than what conventional wisdom suggests. Our extensive evaluation, based on public reward benchmark and HuggingFace Open LLM Leaderboard, demonstrates that our approach compares favorably to state-of-the-art methods that rely solely on demonstration data.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 12942
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