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English(EN) Higher-order pruning of experts in mixture-of-experts language models

MoE模型获得更智能的剪枝、检索和推理效率

作者 PulseAugur 编辑部 · [5 个来源] ·

研究人员正在探索用于混合专家（MoE）语言模型的先进技术，以提高其效率和性能。一篇论文介绍了HOPE（Higher-Order Pruning of Experts），一种考虑专家协同交互的新型剪枝目标，在稀疏率较高和复杂代理任务上表现优于现有方法。另一项研究表明，MoE模型可以作为强大且高效的信息检索系统，在激活参数较少和编码时间较短的情况下优于密集模型。此外，一个名为DynaExq的系统在推理过程中动态量化专家，以管理单GPU上的内存限制，与静态量化和卸载方法相比，提高了准确性和吞吐量。 AI

影响 MoE模型的这些进步有望带来更高效、更强大的AI系统，可能降低计算成本并提高在检索和代理工作负载等复杂任务上的性能。

排序理由 多篇研究论文详细介绍了混合专家（MoE）语言模型技术的进展。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →

MoE模型获得更智能的剪枝、检索和推理效率

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多篇研究论文详细介绍了混合专家（MoE）语言模型技术的进展。
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报道来源 [5]

  1. arXiv cs.AI TIER_1 English(EN) · Alex M. Tseng, Prannay Kaul, Luca Zancato, Wei Xia, Stefano Soatto ·

    混合专家语言模型中的专家高阶剪枝

    arXiv:2609.18916v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most direct approach for reducing this parameter count, yet existing methods make pru…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    混合专家语言模型中的专家高阶剪枝

    Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most direct approach for reducing this parameter count, yet existing methods make pruning decisions for each expert independently, and …

  3. arXiv cs.CL TIER_1 English(EN) · Anubhav Shrestha, Safal Shrestha, Minwu Kim, Torsten Suel, Keith Ross ·

    Mixture-of-Experts 语言模型可以成为强大且高效的检索器

    arXiv:2609.13486v1 Announce Type: cross Abstract: Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectiveness improving as backbones grow in size. However, every query and document must pa…

  4. arXiv cs.LG TIER_1 English(EN) · Kexin Chu, Dawei Xiang, Zixu Shen, Yiwei Yang, Zecheng Liu, Wei Zhang ·

    面向可扩展专家混合模型推理的动态专家量化

    arXiv:2511.15015v4 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying MoE models on a single, memory-limited GPU remains difficult because expert weight…

  5. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Keith Ross ·

    Mixture-of-Experts 语言模型可以成为强大且高效的检索器

    Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectiveness improving as backbones grow in size. However, every query and document must pass through the full model, so encoding cost increa…

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