Author

P. Amortila

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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

#machine learning Jul 2026

Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning

This work proposes a game-theoretic framework that gives this reward-retention trade-off an explicit statistical interpretation, and provides a principled method for learning this equilibrium coefficient via reduction to the KL-regularized RL objective, thus allowing for flexible integration into standard fine-tuning p...

Keegan Harris, Brian Lee, Ian Waudby-Smith et al. · arXiv.org · 0 citations