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Evidence record 11843 · automatically gathered

When Does Muon Help Agentic Reinforcement Learning?

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 final-window validation success from 0.290 to 0.546 (+88%); high-rate AdamW controls retain no post-update success. The

Record details

Published: 17 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 20 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Regulation · Shared topic: Agents & autonomy

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How to cite this record

ethics.ai (17 July 2026), “When Does Muon Help Agentic Reinforcement Learning?,” evidence record 11843, https://ethics.ai/record/11843 (originally published by arXiv cs.AI).

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