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English(EN) Tune the Baseline First: Most of Muon's Scaled Weight Decay Gain Is a Tuning Gap, but Its Small Real Advantage Grows With Model Size

Muon 优化器研究揭示了复杂的预训练动态和微调权衡

作者 PulseAugur 编辑部 · [4 个来源] ·

近期研究探讨了 Muon 优化器在大语言模型预训练中的性能，揭示了与经典梯度下降理论不同的细微行为。一项研究表明，Muon 在大步长下的动态行为偏离了稳定性边缘（EoS）的图景，损失中性和时间对齐独立响应学习率和批量大小。另一篇论文研究了 Muon 和 AdamW 之间的微调差距，发现经过广泛预训练后，尤其随着模型尺寸的增加，AdamW 的性能可能优于 Muon。第三项分析表明，虽然 Muon-SW 提供了随模型尺寸增长的微小实际优势，但其报告的效率提升大部分源于调优差异而非内在优越性。 AI

影响 关于 Muon 性能的细微发现可能会影响未来 LLM 的优化器选择和预训练策略。

排序理由 多篇 arXiv 论文详细介绍了关于 LLM 预训练的 Muon 优化器的研究。

在 arXiv cs.CL 阅读 →

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Muon 优化器研究揭示了复杂的预训练动态和微调权衡

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多篇 arXiv 论文详细介绍了关于 LLM 预训练的 Muon 优化器的研究。
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报道来源 [4]

  1. arXiv cs.LG TIER_1 English(EN) · Yanzhe Chen, Qifang Zhao, Xiaoxiao Xu, Fanghui Liu ·

    Muon 在 LLM 预训练中触及稳定性的边缘

    arXiv:2609.34915v2 Announce Type: replace Abstract: Muon is increasingly used for language-model pretraining, yet its large-step dynamics are not captured by the classical edge-of-stability (EoS) picture of gradient descent (GD). In GD, loss neutrality, equal-magnitude update rev…

  2. arXiv cs.LG TIER_1 English(EN) · Catherine Li, Jingchu Gai, Andrej Risteski, Jacob Mitchell Springer, Aditi Raghunathan ·

    预训练数据规模逆转 Muon-AdamW 微调差距

    arXiv:2611.03633v2 Announce Type: replace Abstract: Muon has recently emerged as a promising optimizer for pretraining large language models, offering a speedup over AdamW in minimizing pretraining loss. However, it remains unclear whether Muon's advantage persists after fine-tun…

  3. arXiv cs.CL TIER_1 English(EN) · Esther Xin ·

    先调基线：Muon 的缩放权重衰减增益大部分是调优差距，但其微小真实优势随模型规模增大而增长

    arXiv:2611.02529v1 Announce Type: new Abstract: Scaled weight decay for Muon (Muon-SW) has been reported to train language models more efficiently than constant decoupled weight decay, but such comparisons usually reuse one learning-rate (LR) and weight-decay (WD) setting for bot…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    预训练数据规模逆转 Muon-AdamW 微调差距

    Muon has recently emerged as a promising optimizer for pretraining large language models, offering a speedup over AdamW in minimizing pretraining loss. However, it remains unclear whether Muon's advantage persists after fine-tuning. In this paper, we study the downstream behavior…

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