AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding
Block-wise semi-autoregressive decoding is the standard inference paradigm for diffusion large language models (DLMs), but it imposes a strict dependency between blocks: the next block cannot begin until the current block is fully decoded or its denoising budget is exhausted. We observe that once a block exposes a reliable delimiter boundary or stable semantic prefix, continuation generation need not wait for every residual token to be resolved. We propose AsyncLane, a training-free decoding scheduler that decouples refinement from advancement. AsyncLane forks a generate lane at observed delimiter boundaries into a refine lane and a continuation generate lane: the prefix remains editable,...
| Authors | Yingxuan Ren, Yuxuan Lou, Yong Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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