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English(EN) SPARE: Sparsity Allocation via Routing Entropy for Efficient MoEs

SPARE方法通过定向剪枝专家来提高MoE模型效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了SPARE（Sparsity Allocation via Routing Entropy，通过路由熵进行稀疏分配），一种新颖的无需训练的方法，旨在通过减少其内存占用量来提高混合专家（MoE）模型的效率。SPARE根据令牌路由的熵来确定每层需要剪枝的专家数量，从而允许在路由分散的层中进行更积极的剪枝。该方法已在Mixtral、Qwen3和DeepSeek等各种MoE模型上展示了准确性的提高，尤其是在高剪枝率下。 AI

影响 该方法可以显著降低部署大型MoE模型的计算和内存需求，使其更易于访问。

排序理由 该集群包含一篇详细介绍优化AI模型架构新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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SPARE方法通过定向剪枝专家来提高MoE模型效率

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该集群包含一篇详细介绍优化AI模型架构新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yifan Yang, Hanling Yi, Dongquan Yang, Feng Lin, Rong Xiao, Aijun Li, Xiaotian Yu ·

    SPARE: Sparsity Allocation via Routing Entropy for Efficient MoEs

    arXiv:2610.29662v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale model capacity efficiently, yet their substantial memory footprint poses a major barrier to deployment. Expert pruning reduces this footprint, but most methods focus on which experts to…

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