The LEASE algorithm represents a novel advancement in offline preference-based reinforcement learning by addressing key challenges such as reward design and the reliance on real-time human feedback. Central to its innovation is the use of a learned transition model, which significantly enhances sample efficiency. This improvement allows the algorithm to acquire preference labels more easily, thereby facilitating better learning outcomes without extensive human intervention. By focusing on offline settings, LEASE reduces the need for continuous human input during training, which is often a bottleneck in reinforcement learning applications. The approach directly tackles the problem of efficiently learning from preferences, a critical issue in the field. As reported in recent research published on arXiv, LEASE’s design contributes positively to both sample efficiency and the ease of obtaining preference data. This development aligns with ongoing efforts to make reinforcement learning more practical and scalable in real-world scenarios.