  Magnitude pruning is a common, effective technique to identify sparse
subnetworks at little cost to accuracy. In this work, we ask whether a
particular architecture's accuracy-sparsity tradeoff can be improved by
combining pruning information across multiple runs of training. From a shared
ResNet-20 initialization, we train several network copies (\emph{siblings}) to
completion using different SGD data orders on CIFAR-10. While the siblings'
pruning masks are naively not much more similar than chance, starting sibling
training after a few epochs of shared pretraining significantly increases
pruning overlap. We then choose a subnetwork by either (1) taking all weights
that survive pruning in any sibling (mask union), or (2) taking only the
weights that survive pruning across all siblings (mask intersection). The
resulting subnetwork is retrained. Strikingly, we find that union and
intersection masks perform very similarly. Both methods match the
accuracy-sparsity tradeoffs of the one-shot magnitude pruning baseline, even
when we combine masks from up to $k = 10$ siblings.
