arXiv Artificial Intelligence

Optimizing Large Language Models with Chained LMOs

2 hours ago 09.10.2026 07:00 👁 2 views
Optimizing Large Language Models with Chained LMOs

Quick summary

arXiv:2610.10975v1 Announce Type: cross Abstract: Muon has motivated a growing family of optimizers that compose multiple matrix normalizations, but these methods remain fragmented and lack a unified perspective. We introduce chained linear minimization oracles (chained LMOs), which cast these methods as compositions of LMOs. Despite their empirical success, many chains fall outside the standard LMO framework and can diverge on smooth convex objectives. To explain why composition can nevertheless help, we turn to linear associative memory and show that chaining can improve over Muon under anis

Key takeaways

  • arXiv:2610.10975v1 Announce Type: cross Abstract: Muon has motivated a growing family of optimizers that compose multiple matrix normalizations, but these methods remain fragmented and lack a unified perspective.
  • We introduce chained linear minimization oracles (chained LMOs), which cast these methods as compositions of LMOs.
  • Despite their empirical success, many chains fall outside the standard LMO framework and can diverge on smooth convex objectives.

Why it matters

“Optimizing Large Language Models with Chained LMOs” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗