Rethinking Sample Utility for Coreset Selection Under Extreme Compression

Coreset selection provides a practical alternative to dataset distillation by pre- serving real, inspectable samples, yet existing methods degrade sharply under extreme compression. We attribute this failure to two mismatches: criteria de- signed for larger budgets undervalue prototypical samples, while fixed evaluators cannot capture how sample utility changes with the evolving coreset. We pro- pose Model-State-Conditioned Coreset Selection (MCCS), which initializes with prototypical samples, updates utility scores using a model trained on the current subset, and filters already-learned samples to prioritize complementary informa- tion. Across CIFAR-10 and ImageNet-1K, MCCS achieves the best or tied-best coreset performance at every budget from 1 to 200 images per class. MCCS also surpasses dataset distillation as the budget or dataset complexity increases and transfers effectively across downstream tasks and architectures. These results es- tablish model-state-conditioned utility estimation as a key requirement for coreset selection under extreme compression.