  Dataset distillation aims to synthesize a small number of images per class
(IPC) from a large dataset to approximate full dataset training with minimal
performance loss. While effective in very small IPC ranges, many distillation
methods become less effective, even underperforming random sample selection, as
IPC increases. Our examination of state-of-the-art trajectory-matching based
distillation methods across various IPC scales reveals that these methods
struggle to incorporate the complex, rare features of harder samples into the
synthetic dataset even with the increased IPC, resulting in a persistent
coverage gap between easy and hard test samples. Motivated by such
observations, we introduce SelMatch, a novel distillation method that
effectively scales with IPC. SelMatch uses selection-based initialization and
partial updates through trajectory matching to manage the synthetic dataset's
desired difficulty level tailored to IPC scales. When tested on CIFAR-10/100
and TinyImageNet, SelMatch consistently outperforms leading selection-only and
distillation-only methods across subset ratios from 5% to 30%.
