From Demonstrations to Rewards: Alignment Without Explicit Human Preferences
This addresses the data and complexity issues in aligning AI models for developers and researchers, offering a more flexible approach when only demonstration data is available, though it is incremental as it builds on inverse reinforcement learning principles.
The paper tackles the challenge of aligning large models with human preferences by proposing a method that learns a reward model directly from demonstration data, eliminating the need for explicit preference data, and shows competitive performance on benchmarks like HuggingFace Open LLM Leaderboard and MT-Bench compared to state-of-the-art methods using only demonstrations.
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 demonstration 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, HuggingFace Open LLM Leaderboard and MT-Bench, demonstrates that our approach compares favorably to state-of-the-art methods that rely solely on demonstration data.