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AdamW outperforms Muon optimizer in LLM fine-tuning with large datasets

By PulseAugur Editorial · [1 sources] ·

A new paper investigates the performance of the Muon optimizer compared to AdamW in large language model pretraining. While Muon initially shows speed advantages, the research reveals that AdamW outperforms Muon after fine-tuning, particularly with larger pretraining datasets. This reversal is explained by the margin-to-spread ratio, which indicates that AdamW becomes more effective as the data budget increases. AI

IMPACT Suggests that AdamW may be a more robust choice than Muon for LLM fine-tuning, especially with extensive pretraining data.

RANK_REASON Research paper analyzing optimizer performance in LLM pretraining and fine-tuning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

AdamW outperforms Muon optimizer in LLM fine-tuning with large datasets

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Research paper analyzing optimizer performance in LLM pretraining and fine-tuning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. 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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