Unlock: Iterative Magnitude Pruning and the Lottery Ticket Hypothesis

Iterative magnitude pruning repeatedly trains, prunes, rewinds, and retrains a network to search for sparse subnetworks that still learn well. The point is not cheap training; the point is understanding trainable sparsity, rewind stability, and when a sparse mask still preserves optimization geometry.