Minimal Ingredients for Reward Assignment from Expert Demonstrations

An arXiv preprint, arXiv:2506.06793, explores the essential framework for reward allocation in imitation learning, where agents learn from expert demonstrations. This study, categorized as a replace-cross type, tackles a significant issue in both offline and online imitation learning: the challenge of assigning rewards from limited demonstrations. The authors argue that a prevalent approach—rewarding learner trajectories based on their similarity to expert demonstrations—forms the foundation of numerous existing techniques, yet the fundamental components influencing performance remain insufficiently examined. They conduct a thorough analysis of two design dimensions: proximity approximation and temporal alignment. Their results across 32 benchmarks, utilizing three downstream reinforcement learning (RL) algorithms, reveal that in offline scenarios, proximity alone suffices for an effective reward structure. Conversely, lightweight temporal alignment yields consistent but modest improvements. These insights indicate that minimal reward assignment can be impactful, suggesting pathways for developing simpler and more efficient imitation learning algorithms. The paper is accessible on arXiv and was last revised in June 2025.