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Research: Best AI optimizer changes with batch size, challenging scaling rules

By PulseAugur Editorial · [1 sources] ·

A new research paper challenges the conventional wisdom that the best optimizer for training language models remains consistent across different batch sizes. The study demonstrates that no single scaling rule for the 'Muon' optimizer consistently performs well across various training conditions. Furthermore, the research indicates that the optimal optimizer for pretraining language models can change significantly as the batch size is adjusted, even after thorough hyperparameter tuning. AI

RANK_REASON Research paper analyzing optimizer performance with varying batch sizes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Research: Best AI optimizer changes with batch size, challenging scaling rules

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Research paper analyzing optimizer performance with varying batch sizes. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 Deutsch(DE) · Xingyu Dang, Kaiyue Wen, Sadhika Malladi ·

    The Best Optimizer Depends on Batch Size

    arXiv:2610.08975v1 Announce Type: new Abstract: A plethora of new adaptive optimizers are designed to efficiently estimate and use minibatch gradient statistics to shape parameter updates, but they are typically benchmarked at a single batch size. Hyperparameter scaling rules pro…

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