What Makes a Good Unmasking Policy? A Controlled Study of Learned Unmasking in Diffusion Language Models

Jinya Sakurai, Xun Xu, Jaehong Yoon · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31576 · open-access PDF

Abstract

Masked diffusion language models generate tokens in parallel, but their efficiency depends on which predictions are committed. Learned unmasking policies improve the speed--accuracy trade-off, yet differ in supervision, inputs, and capacity, obscuring which choices matter. We view supervised policies as distilling a decoding procedure through a teacher and examine independently what the teacher labels as committable, on which states the policy imitates it, what the policy observes, and how much the number of function evaluations (NFE) must decrease to offset policy overhead. We find that label

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