When Does Muon Help Agentic Reinforcement Learning?

Muon optimizer shows measurable gains over standard optimizers in agentic reinforcement learning, with guidelines for when to prefer it. This matters operationally because optimizer choice directly impacts sample efficiency and training stability in agentic systems. Builders should evaluate Muon for tasks with sparse rewards or long-horizon credit assignment, where it reduces variance and accelerates convergence. The primary shift is a potential reduction in compute overhead per training run, as Muon may require fewer environment steps to reach policy thresholds. However, integration requires careful tuning; existing workflows using Adam or AdamW are not obsolete but may become suboptimal in specific regimes. A second-order effect: as agentic RL scales, optimizer-level optimizations like Muon could lower the energy cost per capability gain, shifting infrastructure priorities toward supporting optimizer experimentation in training pipelines.