September 2025: Delivered a major feature refinement for the Muon Optimizer in stanford-crfm/levanter by enabling configurable weight decay for AdamW through a dedicated adam_weight_decay parameter, decoupled from Muon internals. The change also adds zero weight decay support with a sensible default to general weight_decay when unset, and includes renaming/refactoring for clearer configuration semantics. No major bug fixes were documented this month; the focus was on improving configurability, API ergonomics, and maintainability to accelerate experimentation and reduce misconfiguration risk. Impact: more precise regularization control, improved reproducibility of training runs, and faster onboarding for engineers working on optimizer hyperparameters. Technologies/skills demonstrated: Python, ML optimizer design, API design and refactoring, and robust configuration management.