JetSpec++: Foresight Parallel Drafting with Self Refinements

Abstract:Speculative decoding's effectiveness is governed by two quantities: the acceptance rate of drafted tokens and the cost of producing them. We show the two objectives can be co-optimized with algorithm-system co-design, and present JetSpec++, which retains a strong learned draft model while hiding its cost almost entirely. JetSpec++ combines three components: intra-block self refinement, which trains the draft model to iteratively correct its own parallel predictions within a block of tokens; inter-block foresight drafting, which begins the next block before the current one is verified; and pipelined drafting and verification, which overlaps the two stages so drafting's marginal wall-clock cost approaches zero. In comparison with state-of-the-art speculative decoding methods, JetSpec++ attains a wall-clock speedup of up to $7.45\times$ over autoregressive (AR) decoding and $1.54\times$ over DSpark, with up to 1,361 tokens per second for a single request on GB200, and an average acceptance length of 8.94 across seven benchmarks, while preserving the target model's output distribution. Our code and models are available at this https URL.

31 pages, 4 figures, 16 tables. Zhaoxiang Feng and Lanxiang Hu contributed equally. Code and models: this https URL

Computation and Language (cs.CL)

arXiv:2610.19792 [cs.CL]

(or arXiv:2610.19792v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.19792 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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