A new trajectory-level theory reveals Muon accelerates early in optimization along the information-bearing river direction but converges slowly near the bottom, unlike gradient descent. With momentum, Muon's orthogonalized updates remove residual scale information, leading to overshooting and oscillation. The study advocates a two-stage approach—using Muon early and switching to gradient descent-like optimizers later—for improved LLM training performance.