When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning
Imitation learning (IL)---training an agent to replicate expert behavior from demonstrations---underpins applications from robotics to language model training. Standard approaches such as Behavior Cloning (BC) are known to suffer from compounding errors and performance plateaus, particularly when the learner cannot perfectly represent the expert's policy (as is typical, e.g., in distillation). Two interventions are widely understood empirically to improve performance: querying the expert interactively along the learner's own trajectories, and using value function estimation en route to generating a policy rather than directly fitting the expert's full action distribution. We investigate t...
| Authors | Luca Viano, Antoine Moulin, Audrey Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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