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Neural networks rely on optimizers that treat each weight matrix as one object, but these matrices have two parts: magnitude and direction. Decoupling these parts could improve training. This approach is tested in a preprint, but its real-world impact is still unclear.
“arXiv:2606.25971v2 Announce Type: replace Abstract: Modern neural network training relies on optimizers such as Adam and Muon which act on each weight matrix as a single object. Yet every weight matrix carries two distinct quantities -- a \…”
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Research
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ENTITY
Adam, Muon, arXiv:2606.25971v2
DECISION
Automated · no editorial override
LAST OBSERVED
Aug 7, 2026