Deutsch(DE) The Best Optimizer Depends on Batch Size

研究：最佳AI优化器随批量大小而变化，挑战定标规则

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

一项新的研究论文挑战了这样一种传统观念：训练语言模型的最佳优化器在不同批量大小下保持一致。该研究表明，'Muon'优化器没有单一的定标规则能在各种训练条件下始终表现良好。此外，研究表明，即使经过彻底的超参数调整，预训练语言模型的最佳优化器也会随着批量大小的调整而发生显著变化。 AI

排序理由研究论文分析了不同批量大小下的优化器性能。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 研究论文分析了不同批量大小下的优化器性能。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
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arXiv cs.LG TIER_1 Deutsch(DE) · Xingyu Dang, Kaiyue Wen, Sadhika Malladi · 2026-10-08 04:00

最佳优化器取决于批量大小

arXiv:2610.08975v1 Announce Type: new Abstract: A plethora of new adaptive optimizers are designed to efficiently estimate and use minibatch gradient statistics to shape parameter updates, but they are typically benchmarked at a single batch size. Hyperparameter scaling rules pro…