English(EN) Momentum in large-batch training: Polyak enlarges the critical batch size, Nesterov improves data efficiency

对大批量机器学习训练中的 Polyak 和 Nesterov 动量方法进行分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

一篇新的研究论文探讨了动量对机器学习模型大批量训练的影响。该研究通过定义一个用于稳定训练的临界学习率来表征风险稳定性，并为 SGD、Polyak 和 Nesterov 动量方法推导了特定公式。研究结果揭示了动量在不同批量大小下的作用如何变化，表明 Polyak 方法在不损失数据效率的情况下增加了临界批量大小以实现更好的并行性，而 Nesterov 方法则减少了噪声累积，从而在处理大批量场景时提高了数据效率。 AI

影响为优化大批量训练提供了理论见解，可能导致更高效的模型开发。

排序理由详细介绍机器学习训练算法理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Jia-Nan Wang, Zixun Huang, Kairui Li, Lei Wu · 2026-09-03 04:00

大规模训练动量：Polyak 扩大临界批次大小，Nesterov 提高数据效率

arXiv:2609.02728v1 Announce Type: new Abstract: We study when and how momentum improves large-batch training in the one-pass regime, using power-law kernel regression as a tractable setting. We first characterize risk stability through the critical learning rate, defined as the l…

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大规模训练动量：Polyak 扩大临界批次大小，Nesterov 提高数据效率

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