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EVICT method speeds up MoE speculative decoding by optimizing verification

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed EVICT, a new method to improve the efficiency of speculative decoding for Mixture-of-Experts (MoE) models. This technique adaptively truncates the draft tree during verification, focusing on cost-effective prefixes to reduce unnecessary computations. EVICT aims to make every verified token count by leveraging drafter signals and offline cost profiles, offering significant speedups over standard decoding methods. AI

影响 Enhances MoE model inference speed, potentially lowering serving costs and enabling faster generation.

排序理由 Academic paper detailing a new method for optimizing MoE speculative decoding.

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

EVICT method speeds up MoE speculative decoding by optimizing verification

报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Lehan Pan, Ziyang Tao, Ruoyu Pang, Xiao Wang, Jianjun Zhao, Yanyong Zhang ·

    Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding

    arXiv:2605.00342v1 Announce Type: new 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 bran…

  2. arXiv cs.CL TIER_1 English(EN) · Yanyong Zhang ·

    Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding

    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 u…

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