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Keller Jordan on X: "Modded-NanoGPT Optimization Benchmark Hundreds of neural network optimizers have been proposed in the literature, recently including dozens citing Muon: MARS, SWAN, REG, ADANA, Newton-Muon, TrasMuon, AdaMuon, HTMuon, COSMOS, Conda, ASGO, SAGE, and Magma, to name a few. The https://t.co/y6RykqhzL2" / X

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To view keyboard shortcuts, press question mark View keyboard shortcuts Home Explore Notifications Chat Grok Premium Bookmarks Creator Studio Articles Profile More Post Tasha @TashaPais Post See new posts Conversation Keller Jordan @kellerjordan0 Modded-NanoGPT Optimization Benchmark Hundreds of neural network optimizers have been proposed in the literature, recently including dozens citing Muon: MARS, SWAN, REG, ADANA, Newton-Muon, TrasMuon, AdaMuon, HTMuon, COSMOS, Conda, ASGO, SAGE, and Magma, to name a few. The majority of this innovation is happening in the public research community. But the community currently lacks a widely accepted, easily accessible way to compare and make sense of the deluge of methods. As a result, promising new ideas get buried, and spurious results go unchallenged. To help address these issues, I'm releasing a new optimization benchmark. It's designed for maximum simplicity and speed: Just a single file containing ~350 lines of plain PyTorch, which can

Modded-NanoGPT Optimization Benchmark Hundreds of neural network optimizers have been proposed in the literature, recently including dozens citing Muon: MARS, SWAN, REG, ADANA, Newton-Muon, TrasMuon, AdaMuon, HTMuon, COSMOS, Conda, ASGO, SAGE, and Magma, to name a few. The majority of this innovation is happening in the public research community. But the community currently lacks a widely accepted, easily accessible way to compare and make sense of the deluge of methods. As a result, promising new ideas get buried, and spurious results go unchallenged. To help address these issues, I'm…

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