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Muon optimizer complicates balancing in Mixture-of-Experts language models

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the balancing challenges in Mixture-of-Experts (MoE) language models, particularly when using the Muon optimizer instead of the more common AdamW. The study found that Muon, when applied to the entire network, significantly increased the load imbalance in the first MoE layer compared to AdamW. This imbalance was observed to be exacerbated by Muon's learning rate and could be mitigated by adjusting the learning rate or by using AdamW for the router specifically, though keeping the router on Muon was found to be slightly more effective at this scale. AI

IMPACT Highlights potential training difficulties with the Muon optimizer for MoE models, suggesting tuning strategies for better load balancing.

RANK_REASON Research paper detailing a specific technical challenge in training MoE models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Muon optimizer complicates balancing in Mixture-of-Experts language models

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Research paper detailing a specific technical challenge in training MoE models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Marcos Hernanz ·

    Muon Makes the First Mixture-of-Experts Layer Harder to Balance

    arXiv:2610.37914v2 Announce Type: replace-cross Abstract: Many open mixture-of-experts (MoE) language models train with Muon instead of AdamW. Most balance the load with a bias on each expert's routing score, adjusted after every step. Their recipes disagree on whether Muon shoul…

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