Muon optimizer nearly doubled an AI agent’s success, showing its reinforcement-learning value depends on credit assignment and learning rate. With GiGPO, which compares actions from repeated states, Muon raised late success from 0.29 to 0.55. An optimizer cannot be judged alone because the surrounding reinforcement-learning setup may decide whether it helps or fails. – arxiv. org/abs/2607.16169
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Users are excited about the Muon Optimizer nearly doubling AI agent success rates in reinforcement learning because it delivers a massive jump from a single optimizer change.
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@rohanpaul_ai Muon prevents overfitting We can see how bad Adam W has been all this time
@rohanpaul_ai The reward setup determines whether the optimizer helps. Good paper finding.
@rohanpaul_ai The RL algorithm wrapping the optimizer decides if Muon helps or fails
@rohanpaul_ai the setup clearly matters as much
@rohanpaul_ai Agentic success rates doubling from a single optimizer change is a massive jump.
@rohanpaul_ai jump from 0.29 to 0.55 is wild shows how much the RL wrapper matters, not just the optimizer