Author

Audrey Huang

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Preprint Jul 2026

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

This work introduces OVI, an interactive on-policy IL algorithm that is statistically efficient whenever the learner can represent the expert's value function and computationally efficient given access to a linear maximization oracle, and introduces a negative result showing that interaction is necessary.

Luca Viano, Antoine Moulin, Audrey Huang et al. · 0 citations