Utility Boundary of Dataset Distillation: Scaling and Configuration-Coverage Laws

In one paragraph

Proposes a unified 'configuration-dynamics-error' framework that reformulates gradient-, distribution- and trajectory-matching dataset distillation as interchangeable surrogates reducing the same generalization error, deriving a scaling law bounding error against distilled sample size (explaining observed performance saturation) and a coverage law showing the required sample size grows linearly with training-configuration diversity, both with matching upper and lower bounds confirmed experimentally.