When Does Muon Help Agentic Reinforcement Learning?

Kai Ruan, Jinghao Lin, Zihe Huang et al.1 min readarXivJul 17, 2026

Key Takeaway for Students

Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post-training remains unclear. We study vanilla Muon in sparse-reward agentic RL through matched single-seed comparisons with AdamW on ALFWorld using Qwen2.5-0.5B-Instruct. Under Group-in-Group Policy Optimization (GiGPO), applying Muon only to hidden weight matrices raises

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Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post-training remains unclear. We study vanilla Muon in sparse-reward agentic RL through matched single-seed comparisons with AdamW on ALFWorld using Qwen2.5-0.5B-Instruct. Under Group-in-Group Policy Optimization (GiGPO), applying Muon only to hidden weight matrices raises

This research paper advances Agent and production ML, directly affecting how engineers build, optimize, and ship AI systems. Teams running production LLM workloads need to understand these techniques to stay competitive in hiring and architecture decisions.

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ReAct & tool-use frameworksfunction-calling API patternsagent evaluation harnesses

Interview questions

How would you design a system that applies the core technique from "When Does Muon Help Agentic Reinforcement Learning?" at 10k requests/sec?
What are the production trade-offs of adopting Agent in a latency-sensitive serving stack?
Walk me through the key architectural decision in "When Does Muon Help Agentic Reinforcement Learning?" and when you would NOT use this approach.

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