LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter

Abstract:Speculative decoding accelerates autoregressive inference by verifying multiple draft tokens in a single target forward pass. However, as the context grows, existing state-of-the-art drafters become increasingly expensive, eroding the very efficiency advantage they are designed to provide. We argue that this scaling is unnecessary. A standalone language model must grow with its prefix because it is solely responsible for every token it produces. A drafter, by contrast, only proposes candidates; the target catches and corrects every error before any token is committed. The drafter's decoding cost can therefore be made entirely independent of the prefix length. We introduce LongSpark, a block-diffusion drafter that achieves this by extracting fixed-size, multiscale views from the target's verification pass, thereby eliminating the need for a growing persistent state. Extensive evaluations demonstrate that LongSpark achieves state-of-the-art end-to-end efficiency across multiple model scales and realistic serving conditions. Notably, it delivers the lowest time-per-output-token on long-context tasks while reducing the drafter's context state by several orders of magnitude.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.37029 [cs.LG]

(or arXiv:2609.37029v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.37029 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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