On-Policy Interaction Relaxes Representational Demands in Imitation Learning
A recent study published on arXiv (2607.29617) examines the conditions under which on-policy engagement with an expert enhances imitation learning (IL), a method utilized for training agents through demonstrations in robotics and language model development. The researchers question the prevalent belief that interactive querying and value function estimation are inherently advantageous. Their key conclusion reveals that engaging with an expert alleviates the representational requirements for the learner: rather than needing a model that accurately captures the expert's policy, it suffices for the learner to understand the expert's value function, thereby eliminating the necessity to match the entire action distribution. This finding has significant implications for distillation and scenarios where the learner cannot perfectly emulate the expert. The paper is classified as a cross-type announcement and is accessible via the provided URL.
Key facts
- The paper is titled 'When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning'.
- It is available on arXiv with ID 2607.29617.
- The paper addresses imitation learning (IL), which trains agents to replicate expert behavior from demonstrations.
- Standard approaches like Behavior Cloning (BC) suffer from compounding errors and performance plateaus.
- Two interventions are studied: interactive querying of the expert along the learner's trajectories, and using value function estimation.
- The main finding is that expert interaction relaxes representational demands on the learner.
- The learner only needs to realize the expert's value function, not the full action distribution.
- The paper is relevant to applications in robotics and language model training.
- The announcement type is 'cross'.
Entities
Institutions
- arXiv