新的R&Q策略在无需重新训练的情况下平衡SMoE LLM负载

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-15 04:00

研究人员开发了一种名为Replicate-and-Quantize (R&Q) 的新推理时策略，以解决稀疏专家混合（SMoE）大型语言模型中的负载不平衡问题。该方法在无需重新训练或修改路由机制的情况下，动态地重新平衡专家之间的工作负载。实验表明，R&Q可以将不平衡度降低高达1.4倍，同时保持准确性，从而使SMoE模型在部署时更高效、更可预测。 AI

影响提高了SMoE LLM推理的效率和可预测性，可能降低部署成本。

排序理由详细介绍LLM推理新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍LLM推理新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 85 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zijie Liu, Jie Peng, Jinhao Duan, Zirui Liu, Kaixiong Zhou, Mingfu Liang, Luke Simon, Xi Liu, Zhaozhuo Xu, Tianlong Chen · 2026-07-15 04:00

稀疏专家混合LLM即插即用负载均衡的复制与量化策略

arXiv:2602.19938v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently, delivering strong accuracy under fixed compute budgets. However, SMoE models often suffer from severe load imbalanc…

本文如何被排名

报道来源 [1]

稀疏专家混合LLM即插即用负载均衡的复制与量化策略

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