Parallelism, critical windows, and separations among diffusion language models
A popular selling point of diffusion large language models (dLLMs) is their capacity for parallelism: the ability to generate sequences of text far more efficiently than autoregressive models, which require one forward pass per token. Yet among the many competing paradigms for dLLMs, from masked to uniform to Gaussian diffusion, principled understanding of how these different proposals compare in parallelism remains limited. In this work, we initiate a fine-grained comparison of the capacity for parallelism among these three leading approaches and prove the following: - Uniform and Gaussian diffusion can sample in a number of forward passes which scales with the dual total correlation of ...
| Authors | Sitan Chen, Liye Wang |
|---|---|
| Primary category | cs.LG |
| Published | 17 September 2026 |
| Last updated at source | 18 September 2026 |
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