Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding
#Progressive Refinement Regulation #Diffusion Language Models #Decoding Acceleration #Text Generation #Computational Efficiency
📌 Key Takeaways
- Progressive Refinement Regulation is a new method to speed up decoding in diffusion language models.
- It aims to reduce the computational time required for generating text with these models.
- The approach involves refining outputs progressively rather than in a single pass.
- This could make diffusion-based language models more practical for real-time applications.
📖 Full Retelling
arXiv:2603.04514v1 Announce Type: new
Abstract: Diffusion language models generate text through iterative denoising under a uniform refinement rule applied to all tokens. However, tokens stabilize at different rates in practice, leading to substantial redundant refinement and motivating refinement control over the denoising process. Existing approaches typically assess refinement necessity from instantaneous, step-level signals under a fixed decoding process. In contrast, whether a token has co
🏷️ Themes
AI Acceleration, Language Models
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2603.04514 [Submitted on 4 Mar 2026] Title: Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding Authors: Lipeng Wan , Jianhui Gu , Junjie Ma , Jianguo Huang , Shiguang Sun , Siyuan Li , Xuguang Lan View a PDF of the paper titled Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding, by Lipeng Wan and 6 other authors View PDF HTML Abstract: Diffusion language models generate text through iterative denoising under a uniform refinement rule applied to all tokens. However, tokens stabilize at different rates in practice, leading to substantial redundant refinement and motivating refinement control over the denoising process. Existing approaches typically assess refinement necessity from instantaneous, step-level signals under a fixed decoding process. In contrast, whether a token has converged is defined by how its prediction changes along its future refinement trajectory. Moreover, changing the refinement rule reshapes future refinement trajectories, which in turn determine how refinement rules should be formulated, making refinement control inherently dynamic. We propose \emph{Progressive Refinement Regulation} , a progressive, trajectory-grounded refinement control framework that derives a token-level notion of empirical convergence progress from full decoding rollouts. Based on this signal, PRR learns a lightweight token-wise controller to regulate refinement via temperature-based distribution shaping under a progressive self-evolving training scheme. Experiments show that PRR substantially accelerates diffusion language model decoding while preserving generation quality. Comments: 19 pages, 10 figures, Code available upon publication Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04514 [cs.AI] (or arXiv:2603.04514v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.04514 Focus to learn more arXiv-issued DOI via DataCite Sub...
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