From Demonstrations to Rewards: Alignment Without Explicit Human Preference

This paper addresses a core challenge in aligning large language models (LLMs) with human preferences: the substantial data requirements and technical complexity of current state-of-the-art methods, particularly Reinforcement Learning from Human Feedback (RLHF). The authors propose a novel approach based on inverse reinforcement learning (IRL) that can learn alignment directly from demonstration data, eliminating the need for explicit human preference data required by traditional RLHF methods. This research presents a significant step towards simplifying the alignment of large language models by demonstrating that high-quality demonstration data can be effectively leveraged to learn alignment without the need for explicit and costly human preference annotations. The proposed IRL framework offers a promising alternative or complementary approach to existing RLHF methods, potentially reducing the data burden and technical complexities associated with preference collection and reward modelling.