mlsanityData to 14 Oct 2026

Dion2: A Simple Method to Shrink Matrix in Muon

arXiv 2512.16928 ↗First posted 1 Dec 2025cs.LG cs.DCcited by 13

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Abstract (arXiv)

The Muon optimizer enjoys strong empirical performance and theoretical grounding. However, the super-linear cost of its orthonormalization step introduces increasing overhead with scale. To alleviate this cost, several works have attempted to reduce the size of the matrix entering the orthonormalization step. We introduce Dion2, a much simpler method for shrinking the matrix involved in Muon's computation compared to prior approaches. At a high level, Dion2 selects a fraction of rows or columns at each iteration and orthonormalizes only those. This sampling procedure makes the update sparse, reducing both computation and communication costs which in turn improves the scalability of Muon.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 1 Dec 2025.

Builds on

12 of 20 references.

References · 12

  1. 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
  2. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 660
  3. 2025Kimi K2: Open Agentic Intelligencecited by 567
  4. 2025GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Modelscited by 467
  5. 2025Muon is Scalable for LLM Trainingcited by 428
  6. 2025Training Deep Learning Models with Norm-Constrained LMOscited by 192
  7. 2025Practical Efficiency of Muon for Pretrainingcited by 80
  8. 2024Modular Duality in Deep Learningcited by 77
  9. 2024Scalable Optimization in the Modular Normcited by 68
  10. 2025Dion: Distributed Orthonormalized Updatescited by 68
  11. 2025MuonBP: Faster Muon via Block-Periodic Orthogonalizationcited by 35
  12. 2025FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Modelscited by 2

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