3 citations · 4 across the 8 of their papers we have counts for
Showing 2026 · cs.LGShow all
2 papers · 2 filters
cs.LG2026
LionMuon: Alternating Spectral and Sign Descent for Efficient Training
Arman Bolatov, Artem Riabinin, Nikita Kornilov +4
Pretraining a language model takes enormous compute, and the right optimizer can save a good part of it. Muon's spectral step gives a stronger direction than a sign step, but it is…
cs.LG2026
LionMuon: Alternating Spectral and Sign Descent for Efficient Training
Arman Bolatov, Artem Riabinin, Nikita Kornilov +4
Muon's spectral step gives a stronger direction than a sign step, but it is expensive. Every step runs Newton-Schulz iterations on the full matrix and, in distributed training, an…