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arXiv · 2609.37029

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

Hao-Yuan He·Peng-Fei Liu·Chen Chen·Si Shen·Quan He·Runzhou Liu·Xinqi Li·Zhenhua Dong·Ming Li
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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.

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Hao-Yuan He, Peng-Fei Liu, Chen Chen, Si Shen, Quan He, Runzhou Liu, Xinqi Li, Zhenhua Dong, Ming Li. 2026-10-26. LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter. https://arxiv.org/abs/2609.37029

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