A new research paper demonstrates that the choice of optimizer significantly impacts a Transformer model's capacity and scaling laws, even when the architecture remains identical. The study found that the Muon optimizer achieved linear scaling in representation capacity, a 2.3x improvement over AdamW's weaker scaling, particularly in challenging rare-token regimes. This suggests that optimizers should be considered a primary factor in model scaling, alongside architecture and data, and highlights the potential for co-designing optimizers and architectures for better performance. AI
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IMPACT Highlights that optimizer choice is a critical, under-explored factor in achieving optimal model scaling and representation capacity.
RANK_REASON The cluster contains an academic paper detailing novel research findings on model training.