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English(EN) Approximate Muon with low-rank adapters

新的sMuon方法增强了神经网络的低秩微调

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为sMuon的新方法，以改进神经网络的参数高效微调（PEFT）。该技术通过在低秩设置中近似Muon目标，解决了Muon优化器与常见的LoRA PEFT方法之间的不兼容问题。该实现仅使用矩阵乘法运算，并在监督微调和ReLoRA预训练实验中显示出有利的结果，提供了适度的性能改进。 AI

影响 提高了神经网络微调的效率，有可能以更少的计算资源获得更好的性能。

排序理由 该集群包含一篇研究论文，详细介绍了优化神经网络微调的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的sMuon方法增强了神经网络的低秩微调

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该集群包含一篇研究论文，详细介绍了优化神经网络微调的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ben Anson, Conor Houghton, Edward Milsom ·

    使用低秩适配器近似Muon

    arXiv:2608.14492v1 Announce Type: new Abstract: The Muon optimizer shows clear benefits versus alternatives when pretraining neural networks. However, it is used less frequently for parameter-efficient fine-tuning (PEFT). One potential reason is that the most common PEFT method, …

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