Abstract
What kind of data does a model need in order to learn? Coreset selection makes this question concrete: under a budget, keep the samples most useful for training. Easy-first and geometric coverage criteria can win in different budget regimes, separated by a crossover boundary. We ask whether this boundary is fixed by the data or changes with the target learner. Controlled experiments freeze the selected subsets and manipulate only the training learner. On low-resolution ImageNet-100, doubling ResNet-18's width moves the crossover from 57 to 85 samples per class: the learner changes the relative value of the same samples. A wider sweep reveals an interaction between input grid and capacity. Enlarging the grid while retaining the same image information shifts the boundary left, and this shift weakens as width increases. Stride controls reproduce and reverse the grid effect without changing the input grid; removing only the last downsampling stride is sufficient to recover the leftward shift. Under the native-224px ImageNet-1k protocol, width effects are smaller and depend on the probe: LFrac remains nearly flat, while EL2N shifts modestly right. Swapping the convolutional learning system for a ViT makes coverage win throughout the measured range, even when the easy subsets come from the convolutional proxy. These results establish learner dependence through frozen-subset interventions and identify network structure that can move the boundary. They do not yield a universal scaling law. Their practical implication is direct: a selection strategy's preferred budget regime must be evaluated with respect to the target learner.
Abstract (translated)
URL
https://arxiv.org/abs/2610.00221
