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English(EN) Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling

Seesaw方法通过优化批次大小和学习率调度来加速LLM训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Seesaw的新方法，通过优化批次大小和学习率的调度来加速大型语言模型的训练。该方法在理论上证明了SGD的学习率衰减与批次大小增长之间的等价性，并将其扩展到Adam的代理。实证表明，Seesaw在FLOPs方面与余弦衰减相当，同时将Chinchilla规模模型的训练时间缩短了约36%。 AI

影响 该方法可以显著减少预训练大型语言模型所需的时间和计算资源。

排序理由 该集群包含一篇详细介绍加速LLM训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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Seesaw方法通过优化批次大小和学习率调度来加速LLM训练

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该集群包含一篇详细介绍加速LLM训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Alexandru Meterez, Depen Morwani, Jingfeng Wu, Costin-Andrei Oncescu, Cengiz Pehlevan, Sham Kakade ·

    Seesaw：通过平衡学习率和批次大小调度来加速训练

    arXiv:2510.14717v2 Announce Type: replace-cross Abstract: Increasing the batch size during training -- a ''batch ramp'' -- is a promising strategy to accelerate large language model pretraining. While for SGD, doubling the batch size can be equivalent to halving the learning rate…

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