新方法允许 MoE 模型跳过超过一半的专家

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-18 16:50

研究人员开发了一个名为 Zero-Expert Self-Distillation Adaptation (ZEDA) 的新框架，以提高现有 Mixture-of-Experts (MoE) 语言模型的效率。ZEDA 允许经过后训练的静态 MoE 模型在推理过程中动态地跳过超过一半的专家，同时准确性损失极小。该方法在 Qwen3-30B-A3B 和 GLM-4.7-Flash 模型上进行了测试，展示了显著的推理加速，并且优于现有的动态 MoE 基线。 AI

影响能够显著加速 MoE 模型的推理，可能降低服务成本并提高可访问性。

排序理由该集群包含一篇详细介绍提高模型效率新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Bowen Zhou · 2026-05-18 16:50

训练后MoE可通过自蒸馏跳过一半专家

Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its dynamic variant further reduces computation by adjusting the activated experts in an input-dependent manner. Existing dynamic MoE methods usually rely on pre-training from scratc…

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训练后MoE可通过自蒸馏跳过一半专家

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