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Muon optimizer research reveals complex pretraining dynamics and fine-tuning trade-offs

By PulseAugur Editorial · [4 sources] ·

Recent research explores the performance of the Muon optimizer in large language model pretraining, revealing nuanced behaviors that differ from classical gradient descent theories. One study indicates that Muon's dynamics at large steps deviate from the edge-of-stability (EoS) picture, with loss neutrality and temporal alignment responding independently to learning rate and batch size. Another paper investigates the fine-tuning gap between Muon and AdamW, finding that AdamW can outperform Muon after extensive pretraining, especially as model size increases. A third analysis suggests that while Muon-SW offers a small real advantage that grows with model size, much of its reported efficiency gain stems from tuning differences rather than inherent superiority. AI

IMPACT Nuanced findings on Muon's performance may influence future optimizer choices and pretraining strategies for LLMs.

RANK_REASON Multiple arXiv papers detailing research into the Muon optimizer for LLM pretraining.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Muon optimizer research reveals complex pretraining dynamics and fine-tuning trade-offs

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Multiple arXiv papers detailing research into the Muon optimizer for LLM pretraining.
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COVERAGE [4]

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

    Muon Sublates the Edge of Stability in LLM Pretraining

    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 ·

    Pretraining Data Scale Reverses the Muon-AdamW Fine-Tuning Gap

    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 ·

    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

    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) ·

    Pretraining Data Scale Reverses the Muon-AdamW Fine-Tuning Gap

    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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