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New Muon recipe enhances long-horizon agent training

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new training recipe for long-horizon agents, which are designed to coordinate reasoning, tool use, and environmental feedback over extended periods. This recipe, named Muon, incorporates single-rollout asynchronous learning, token-averaged loss, truncated importance sampling, and auxiliary penalties for length and tool errors. The approach allows for asynchronous updates without waiting for multiple trajectories, leading to higher training success rates compared to baseline methods when applied to repository-level issue resolution. AI

IMPACT This new training recipe could improve the capabilities of AI agents in complex, long-term tasks.

RANK_REASON The cluster describes a research paper detailing a new training methodology for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

New Muon recipe enhances long-horizon agent training

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The cluster describes a research paper detailing a new training methodology for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 Nederlands(NL) · Kai Ruan, Jinghao Lin, Tao Chen, Ziqi Zhou, Qianshan Wei, Zihe Huang ·

    Long-Horizon RL Needs Muon

    arXiv:2610.20256v1 Announce Type: new Abstract: Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories of different lengths and updating the policy while ro…

  2. Hugging Face Daily Papers TIER_1 Nederlands(NL) ·

    Long-Horizon RL Needs Muon

    Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories of different lengths and updating the policy while rollouts are still being collected. We examine a s…

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