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Muon optimizer shows promise in agentic reinforcement learning tasks

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the effectiveness of the Muon optimizer in agentic reinforcement learning (RL) tasks, particularly when applied to sparse-reward environments. The study, using Qwen2.5-0.5B-Instruct on the ALFWorld benchmark, found that incorporating Muon into hidden weight matrices significantly improved success rates compared to the standard AdamW optimizer. The benefits of Muon were observed to be dependent on factors such as the advantage estimator and learning rate, with specific configurations leading to substantial gains in validation success and faster convergence. AI

IMPACT Muon optimizer shows potential to improve agentic reinforcement learning performance, particularly in sparse-reward scenarios.

RANK_REASON The cluster contains a research paper detailing experimental results on an AI optimization technique.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Muon optimizer shows promise in agentic reinforcement learning tasks

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Kai Ruan, Jinghao Lin, Zihe Huang, Ziqi Zhou, Qianshan Wei, Xuan Wang, Hao Sun ·

    When Does Muon Help Agentic Reinforcement Learning?

    arXiv:2607.16169v1 Announce Type: cross Abstract: 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 wit…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    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. U…

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