AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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PolarGD, the orthogonalized update at the core of Muon, is studied on matrix quadratics, where optimizers can be analyzed in closed form, and update directions under line search are compared to remove the effect of learning-rate tuning.
A framework for efficient scaling law construction that formulates data collection as a Bayesian optimization problem is proposed, and metrics for comparing scaling law fitting methods under constrained compute budgets are introduced.
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