When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning

Imitation learning trains agents from expert demonstrations, powering robotics and language-model training, but standard behavior cloning suffers from compounding errors when the learner can\'t perfectly mimic the expert. This paper explains why querying the expert interactively helps: it lets the learner target the expert\'s value function rather than the harder task of replicating its exact policy. The authors introduce OVI, an algorithm exploiting this insight, and prove interaction is necessary for efficiency. This has practical implications for training robots or AI systems from limited or costly expert demonstrations. Authors: Luca Viano, Antoine Moulin, Audrey Huang, Volkan Cevher, Philip Paper: https://arxiv.org/abs/2607.29617v1