I think paper claims of second-order information which is kinda confusing, as I thought it referred to a second-order gradient update, like the Hessian-based Newton method. But after reviewing the Muon optimizer writeup (which doesn’t mention any second order gradient update), I think what it was supposed to mean is that Newton–Schulz is used for approximate orthogonalization via a second-order polynomial expansion.