@inproceedings{wang-etal-2026-accelerate,
title = "Accelerate Speculative Decoding with Sparse Computation in Verification",
author = "Wang, Jikai and
Tan, Jianchao and
Hu, Yuxuan and
Qin, Jiayu and
Sun, Yerui and
Xie, Yuchen and
Cai, Xunliang and
Li, Juntao and
Zhang, Min",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2231/",
pages = "47762--47775",
ISBN = "979-8-89176-462-0",
abstract = "Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel. However, the verification stage often becomes the dominant computational bottleneck, especially for long-context inputs and mixture-of-experts (MoE) models. Existing sparsification methods are designed primarily for token-by-token autoregressive decoding to remove substantial computational redundancy in LLMs. This work systematically adopts different sparse methods on the verification stage of the speculative decoding and identifies structured redundancy across multiple dimensions. Based on these observations, we propose a sparse verification framework that jointly sparsifies attention, FFN, and MoE components during the verification stage to reduce the dominant computation cost. The framework further incorporates an inter-draft token and inter-layer retrieval reuse strategy to further reduce redundant computation without introducing additional training. Extensive experiments across summarization, question answering, and mathematical reasoning datasets demonstrate that the proposed methods achieve favorable efficiency-accuracy trade-offs and can be translated into practical latency reduction with optimized sparse kernels."
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<abstract>Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel. However, the verification stage often becomes the dominant computational bottleneck, especially for long-context inputs and mixture-of-experts (MoE) models. Existing sparsification methods are designed primarily for token-by-token autoregressive decoding to remove substantial computational redundancy in LLMs. This work systematically adopts different sparse methods on the verification stage of the speculative decoding and identifies structured redundancy across multiple dimensions. Based on these observations, we propose a sparse verification framework that jointly sparsifies attention, FFN, and MoE components during the verification stage to reduce the dominant computation cost. The framework further incorporates an inter-draft token and inter-layer retrieval reuse strategy to further reduce redundant computation without introducing additional training. Extensive experiments across summarization, question answering, and mathematical reasoning datasets demonstrate that the proposed methods achieve favorable efficiency-accuracy trade-offs and can be translated into practical latency reduction with optimized sparse kernels.</abstract>
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%0 Conference Proceedings
%T Accelerate Speculative Decoding with Sparse Computation in Verification
%A Wang, Jikai
%A Tan, Jianchao
%A Hu, Yuxuan
%A Qin, Jiayu
%A Sun, Yerui
%A Xie, Yuchen
%A Cai, Xunliang
%A Li, Juntao
%A Zhang, Min
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-accelerate
%X Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel. However, the verification stage often becomes the dominant computational bottleneck, especially for long-context inputs and mixture-of-experts (MoE) models. Existing sparsification methods are designed primarily for token-by-token autoregressive decoding to remove substantial computational redundancy in LLMs. This work systematically adopts different sparse methods on the verification stage of the speculative decoding and identifies structured redundancy across multiple dimensions. Based on these observations, we propose a sparse verification framework that jointly sparsifies attention, FFN, and MoE components during the verification stage to reduce the dominant computation cost. The framework further incorporates an inter-draft token and inter-layer retrieval reuse strategy to further reduce redundant computation without introducing additional training. Extensive experiments across summarization, question answering, and mathematical reasoning datasets demonstrate that the proposed methods achieve favorable efficiency-accuracy trade-offs and can be translated into practical latency reduction with optimized sparse kernels.
%U https://aclanthology.org/2026.findings-emnlp.2231/
%P 47762-47775
Markdown (Informal)
[Accelerate Speculative Decoding with Sparse Computation in Verification](https://aclanthology.org/2026.findings-emnlp.2231/) (Wang et al., Findings 2026)
ACL
- Jikai Wang, Jianchao Tan, Yuxuan Hu, Jiayu Qin, Yerui Sun, Yuchen Xie, Xunliang Cai, Juntao Li, and Min Zhang. 2026. Accelerate Speculative Decoding with Sparse Computation in Verification. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47762–47775, Budapest, Hungary. Association for Computational Linguistics.