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Study finds AdamW dominance challenged by other optimizers, with caveats

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv systematically compares ten deep learning optimizers for language model pretraining, challenging the dominance of AdamW. The research highlights that previous comparisons were often flawed due to unequal hyperparameter tuning and limited evaluation setups. While matrix-based optimizers like Muon and Soap show promise, their speedup over AdamW diminishes significantly with increasing model scale, offering only a 1.1x advantage for models up to 1.2 billion parameters. AI

IMPACT Suggests that optimizers other than AdamW may offer speedups in language model pretraining, though the benefits decrease with model scale.

RANK_REASON Academic paper detailing a systematic study of deep learning optimizers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

Study finds AdamW dominance challenged by other optimizers, with caveats

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Academic paper detailing a systematic study of deep learning optimizers. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Kaiyue Wen, David Hall, Tengyu Ma, Percy Liang ·

    Fantastic Pretraining Optimizers and Where to Find Them

    arXiv:2509.02046v3 Announce Type: replace-cross Abstract: AdamW has long been the dominant optimizer in language model pretraining, despite numerous claims that alternative optimizers offer 1.4 to 2x speedup. We posit that two methodological shortcomings have obscured fair compar…

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