AI RESEARCH
Towards Distillation Guarantees under Algorithmic Alignment for Combinatorial Optimization
arXiv CS.LG
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ArXi:2605.20074v1 Announce Type: new Distillation transfers knowledge from a large model trained on broad data to a smaller, efficient model suitable for deployment. In structured prediction settings, prior knowledge about the task can guide the choice of a target architecture that is algorithmically aligned with the underlying problem. Building on recent learning-theoretic analyses of decision-tree (DT) distillation (Boix-Adsera, 2024), we study when distillation succeeds for combinatorial optimization tasks.