@inproceedings{pan-etal-2026-making,
title = "Making Every Verified Token Count: Adaptive Verification for {M}o{E} Speculative Decoding",
author = "Pan, Lehan and
Tao, Ziyang and
Wang, Xiao and
Zhao, Zhimeng and
Wang, Meng and
Zhang, Yanyong",
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.380/",
pages = "8025--8040",
ISBN = "979-8-89176-462-0",
abstract = "Tree-based speculative decoding accelerates autoregressive generation by verifying multiple draft candidates in parallel, but this advantage weakens for sparse Mixture-of-Experts (MoE) models. As the draft tree grows, different branches activate different experts, expanding the union of activated experts and substantially increasing target-side verification cost. We propose EVICT, a training-free, hyperparameter-free, and lossless adaptive verification method for MoE speculative decoding. EVICT makes every verified token count by truncating the draft tree before target verification and retaining only the cost-effective prefix. It leverages fine-grained drafter signals to estimate candidate benefit, combines them with offline-profiled verification cost, and remains highly compatible with the high-performance graph-based serving framework SGLang. Extensive experiments on diverse MoE backbones and benchmarks show that EVICT achieves up to 2.35$\times$ speedup over autoregressive decoding and an average 1.21$\times$ speedup over the state-of-the-art baseline EAGLE-3, while significantly reducing unnecessary expert activations during verification."
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<abstract>Tree-based speculative decoding accelerates autoregressive generation by verifying multiple draft candidates in parallel, but this advantage weakens for sparse Mixture-of-Experts (MoE) models. As the draft tree grows, different branches activate different experts, expanding the union of activated experts and substantially increasing target-side verification cost. We propose EVICT, a training-free, hyperparameter-free, and lossless adaptive verification method for MoE speculative decoding. EVICT makes every verified token count by truncating the draft tree before target verification and retaining only the cost-effective prefix. It leverages fine-grained drafter signals to estimate candidate benefit, combines them with offline-profiled verification cost, and remains highly compatible with the high-performance graph-based serving framework SGLang. Extensive experiments on diverse MoE backbones and benchmarks show that EVICT achieves up to 2.35\times speedup over autoregressive decoding and an average 1.21\times speedup over the state-of-the-art baseline EAGLE-3, while significantly reducing unnecessary expert activations during verification.</abstract>
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%0 Conference Proceedings
%T Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding
%A Pan, Lehan
%A Tao, Ziyang
%A Wang, Xiao
%A Zhao, Zhimeng
%A Wang, Meng
%A Zhang, Yanyong
%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 pan-etal-2026-making
%X Tree-based speculative decoding accelerates autoregressive generation by verifying multiple draft candidates in parallel, but this advantage weakens for sparse Mixture-of-Experts (MoE) models. As the draft tree grows, different branches activate different experts, expanding the union of activated experts and substantially increasing target-side verification cost. We propose EVICT, a training-free, hyperparameter-free, and lossless adaptive verification method for MoE speculative decoding. EVICT makes every verified token count by truncating the draft tree before target verification and retaining only the cost-effective prefix. It leverages fine-grained drafter signals to estimate candidate benefit, combines them with offline-profiled verification cost, and remains highly compatible with the high-performance graph-based serving framework SGLang. Extensive experiments on diverse MoE backbones and benchmarks show that EVICT achieves up to 2.35\times speedup over autoregressive decoding and an average 1.21\times speedup over the state-of-the-art baseline EAGLE-3, while significantly reducing unnecessary expert activations during verification.
%U https://aclanthology.org/2026.findings-emnlp.380/
%P 8025-8040
Markdown (Informal)
[Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding](https://aclanthology.org/2026.findings-emnlp.380/) (Pan et al., Findings 2026)
ACL