When Does Muon Help Agentic Reinforcement Learning?

  • 类型：arxiv
  • 标识：2607.16169
  • 链接：https://arxiv.org/abs/2607.16169
  • 主分类：agent
  • 形态：method
  • 被引：0
  • 被引来源：Semantic Scholar
  • S2被引：0
  • 影响力被引：0
  • TLDR：A recipe-level operating regime in which fan-in Muon supports a more aggressive stable effective step under shared KL and clipping is identified: the margin is largest when optimization headroom remains and contracts near saturation, after AdamW tuning, or under magnitude matching.
  • 副分类：engineering
  • 待LLM分类：否
  • 标题中文：Muon何时有助于Agentic强化学习？
  • TLDR中文：在一个 recipe 级操作机制中，fan-in Muon 在共享 KL 与 clipping 下支持更激进的稳定有效步长：该余量在优化仍有空间时最大，而在接近饱和、经 AdamW 调参后或使用 magnitude matching 时收缩。
  • 来源文件：
  • /inbox/tom/_candidates/2026-07-20-agent-rag-longcontext-candidates.json
  • [S2 enrich]