English(EN) Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing

MoE模型：通过预测算法实现专家负载稳定

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种方法来稳定混合专家（MoE）模型中的专家负载分配，解决了阻碍并行化和资源利用率的专家负载波动问题。通过分析训练过程中的专家负载，他们识别了瞬态和稳定状态，并采用预测算法来准确预测专家负载比例。对于GPT3 350M模型，该方法在预测未来1000步和2000步的负载时，平均误差率分别约为1.3%和1.8%。这项工作为MoE模型训练中的专家放置和资源分配提供了指导。 AI

影响通过稳定专家负载分配，提高了混合专家模型的训练效率。

排序理由学术论文，详细介绍了一种提高MoE模型训练效率的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Peizhuang Cong, Aomufei Yuan, Shimao Chen, Yuxuan Tian, Bowen Ye, Yilun Yao, Tong Yang · 2026-10-15 04:00

预测全靠 MoE：专家负载分配从波动走向稳定

arXiv:2404.16914v2 Announce Type: replace-cross Abstract: MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing parameters. The learning sparse gating network selects a set of experts for each …

本文如何被排名

报道来源 [1]

预测全靠 MoE：专家负载分配从波动走向稳定

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