Unifying Masked Diffusion Models with Various Generation Orders and Beyond

• URL: http://arxiv.org/abs/2602.02112v1
• Date: Mon, 02 Feb 2026 13:54:32 GMT
• Title: Unifying Masked Diffusion Models with Various Generation Orders and Beyond
• Authors: Chunsan Hong, Sanghyun Lee, Jong Chul Ye,
• Abstract summary: Masked diffusion models (MDMs) are a potential alternative to autoregressive models (ARMs) for language generation.<n>We propose order-expressive masked diffusion model (OeMDM) for a broad class of diffusion generative processes.<n>We introduce learnable-order masked diffusion model (LoMDM) which jointly learns the generation ordering and diffusion backbone.
• Score: 56.70289720766803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Masked diffusion models (MDMs) are a potential alternative to autoregressive models (ARMs) for language generation, but generation quality depends critically on the generation order. Prior work either hard-codes an ordering (e.g., blockwise left-to-right) or learns an ordering policy for a pretrained MDM, which incurs extra cost and can yield suboptimal solutions due to the two-stage optimization. Motivated by this, we propose order-expressive masked diffusion model (OeMDM) for a broad class of diffusion generative processes with various generation orders, enabling the interpretation of MDM, ARM, and block diffusion in a single framework. Furthermore, building on OeMDM, we introduce learnable-order masked diffusion model (LoMDM), which jointly learns the generation ordering and diffusion backbone through a single objective from scratch, enabling the diffusion model to generate text in context-dependent ordering. Empirically, we confirm that LoMDM outperforms various discrete diffusion models across multiple language modeling benchmarks.

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