Author

Dylan J. Foster

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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. · arXiv.org · 0 citations

Preprint Aug 2026

TailSFT: Filtered Fine-Tuning Improves Post-Training Performance

A simple modification to supervised fine-tuning, TailSFT, which filters out already fit sequences during training, thereby focusing learning on under-modeled regions, or the tail, of the data distribution, and introduces a lightweight diagnostic for identifying settings where TailSFT is most likely to help.

Sadhika Malladi, Samy Jelassi, Dylan J. Foster et al. · 2 citations