Accelerating Diffusion Language Models via Structured Suffix Modeling
Diffusion Language Models (DLMs) exhibit strong parallel decoding capabilities by denoising multiple tokens in a single generation step. However, this parallelism comes with substantial computational overhead, as each step requires interactions with all suffix tokens. Existing methods typically reduce this cost by retaining only a local suffix window as a substitute for the full suffix. Despite their effectiveness, these methods overlook the structural heterogeneity across suffix regions and re-initialize suffix tokens with identical representations at each timestep. To this end, we propose a structured suffix modeling method for efficient DLM inference. Specifically, we divide the suffix...
| Authors | Zifeng Cheng, Keda Li, Zhiwei Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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