D-Loop: Looped Diffusion Drafting for Speculative Decoding
Block diffusion accelerates speculative decoding by drafting multiple tokens in one forward pass. However, each position predicts a marginal distribution without observing earlier proposed tokens, limiting draft quality and acceptance length. We identify a concrete failure, the \emph{repetition trap}, in which neighboring positions produce redundant copies of the same token. We explain this tendency theoretically and empirically examine its association with shorter accepted drafts. Recent methods refine marginal predictions with an additional causal head or a separately trained drafter, increasing parameter storage and introducing separate training objectives. We instead propose D-Loop, w...
| Authors | Kecheng Chen, Yuyang He, Cheng Gong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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