Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding
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 ref...
| Authors | Lipeng Wan, Jianhui Gu, Junjie Ma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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