Multi-Mask Diffusion Language Models for Few-Step Generation
Proposes Multi-Mask Diffusion Model (MultiMDM) that addresses the terminal entropy issue in masked diffusion models for few-step generation by introducing multiple mask states, enabling high-quality text generation with few steps.
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[Submitted on 22 Jul 2026]
Title:Multi-Mask Diffusion Language Models for Few-Step Generation
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Abstract:Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging. In MDMs, all forward trajectories collapse to a single fully masked state, leaving no terminal entropy for consistency-style few-step generation. While recent few-step alternatives based on uniform-state diffusion avoid this degeneracy, it becomes harder to distinguish clean tokens from noise than MDMs, which usually harms modeling quality and training efficiency. In this work, we propose a multi-mask diffusion model (MultiMDM) that preserves the masking structure towards few-step generation. In the forward process, each clean token is first pushed towards a designated mask and then gradually mixes over the mask set. As a result, the backward process has a drafting capability by predicting a designated mask before refining to a clean token. We derive a closed-form ELBO training objective for MultiMDM that supports continual training from pretrained MDMs. In addition, we formulate a purely discrete-state consistency distillation scheme, with a shared-Gumbel coupling to reduce pathwise entropy. Experiments on pretraining and distillation show that MultiMDM provides an effective foundation for principled few-step generation.
Comments: 38 pages; Accepted at COLM 2026
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2607.19686 [cs.CL]
(or arXiv:2607.19686v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.19686
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sijin Chen [view email] [v1] Wed, 22 Jul 2026 02:35:40 UTC (11,333 KB)
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