Improving Sampling for Masked Diffusion Models via Information Gain

arXiv:2602.18176v3 Announce Type: replace Abstract: Masked Diffusion Models (MDMs) enable flexible decoding orders, yet existing samplers remain largely greedy, selecting locally certain tokens without accounting for their downstream effects. We show that this myopia can increase cumulative uncertainty and lead to suboptimal generation. To address this, we propose the **Info-Gain Sampler**, a training-free decoding method that uses the bidirectional structure of MDMs to balance immediate uncertainty with the information gained over remaining masked positions. Across reasoning, coding, creative

Source: arXiv cs.CL — read the full report at the original publisher.

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