UGC provides a principled way to design adaptive masking schedules for discrete diffusion that are provably near-optimal, with potential √d speedups over fixed schedules by concentrating computational effort where data geometry demands it.
This paper introduces unmasking growth complexity (UGC), a geometric measure that controls how discrete diffusion models should reveal information during sampling. The key insight is that UGC increments directly bound discretization error, enabling optimized sampling schedules that adapt to data structure.