Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers
Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still face a severe quality-speed trade-off: accelerating decoding by revealing multiple tokens often causes substantial quality degradation. We attribute this dilemma to a train-inference mismatch amplified by irreversible decoding. While training reconstructs tokens from randomly corrupted states, efficient inference requires an adaptive denoising order, where easier tokens are revealed earlier and context-dependent ones are deferred. This view motivates two complementary methods: an inference-time method that makes parallel decoding revokable, and a training-time extension that distills the ...
| Authors | Fanqin Zeng, Feng Hong, Geng Yu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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