RMNP preconditions matrix updates via row-wise L2 normalization instead of Newton-Schulz iteration, reducing complexity to O(mn) while matching Muon's non-convex convergence rate and empirical performance.
Analytic insights into structure and rank of neural network Hessian maps.Advances in Neural Information Processing Systems, 34:23914–23927
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RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization
RMNP preconditions matrix updates via row-wise L2 normalization instead of Newton-Schulz iteration, reducing complexity to O(mn) while matching Muon's non-convex convergence rate and empirical performance.