Dion2: A Simple Method to Shrink Matrix in Muon
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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
- 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
- 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 660
- 2025Kimi K2: Open Agentic Intelligencecited by 567
- 2025GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Modelscited by 467
- 2025Muon is Scalable for LLM Trainingcited by 428
- 2025Training Deep Learning Models with Norm-Constrained LMOscited by 192
- 2025Practical Efficiency of Muon for Pretrainingcited by 80
- 2024Modular Duality in Deep Learningcited by 77
- 2024Scalable Optimization in the Modular Normcited by 68
- 2025Dion: Distributed Orthonormalized Updatescited by 68
- 2025MuonBP: Faster Muon via Block-Periodic Orthogonalizationcited by 35
- 2025FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Modelscited by 2
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Citing papers per year: 2026: 13.
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- 2026The Newton-Muon Optimizercited by 18
- 2026Improved Convergence Rates of Muon Optimizer for Nonconvex Optimizationcited by 9
- 2026Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Lesscited by 5
- 2026Spectral Scaling Laws of Muoncited by 4
- 2026Anytime Training with Schedule-Free Spectral Optimizationcited by 3
- 2026Tight Complexity of Muon under Heavy-Tailed Noisecited by 2
- 2026Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimizationcited by 2
- 2026Scale Weight Decay and Train Bettercited by 2
- 2026When and Why Grouping Attention Heads Accelerates Muon Optimizationcited by 1
- 2026Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Losscited by 1
- 2026Scaling Muon for Diffusion Transformerscited by 1
- 2026Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimizationcited by 0
- 2026MuonIO: Principled Norm-Aware Descent for Embedding Tables and Language Model Heads2 Oct 2026
- 2026Scaling Muon for Diffusion Transformers21 Aug 2026
- 2026Scale Weight Decay and Train Better26 Jul 2026
- 2026Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimization25 Jun 2026
- 2026Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss4 Jun 2026
- 2026Spectral Scaling Laws of Muon2 Jun 2026
- 2026Anytime Training with Schedule-Free Spectral Optimization21 May 2026
- 2026Tight Complexity of Muon under Heavy-Tailed Noise18 May 2026
- 2026When and Why Grouping Attention Heads Accelerates Muon Optimization9 May 2026
- 2026Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimization7 May 2026
- 2026Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less7 May 2026
- 2026The Newton-Muon Optimizer1 Apr 2026
1 more
- 2026Scaling Muon for Diffusion Transformerscited by 1
- 2026Scaling Muon for Diffusion Transformers21 Aug 2026
- 2026The Newton-Muon Optimizercited by 18
- 2026Improved Convergence Rates of Muon Optimizer for Nonconvex Optimizationcited by 9
- 2026Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Lesscited by 5
- 2026Spectral Scaling Laws of Muoncited by 4
- 2026Anytime Training with Schedule-Free Spectral Optimizationcited by 3
- 2026Tight Complexity of Muon under Heavy-Tailed Noisecited by 2
- 2026Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimizationcited by 2
- 2026Scale Weight Decay and Train Bettercited by 2
- 2026When and Why Grouping Attention Heads Accelerates Muon Optimizationcited by 1
- 2026Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Losscited by 1
- 2026Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimizationcited by 0
- 2026MuonIO: Principled Norm-Aware Descent for Embedding Tables and Language Model Headscited by 0
- 2026MuonIO: Principled Norm-Aware Descent for Embedding Tables and Language Model Heads2 Oct 2026
- 2026Scale Weight Decay and Train Better26 Jul 2026
- 2026Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimization25 Jun 2026
- 2026Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss4 Jun 2026
- 2026Spectral Scaling Laws of Muon2 Jun 2026
- 2026Anytime Training with Schedule-Free Spectral Optimization21 May 2026
- 2026Tight Complexity of Muon under Heavy-Tailed Noise18 May 2026
- 2026When and Why Grouping Attention Heads Accelerates Muon Optimization9 May 2026
- 2026Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimization7 May 2026
- 2026Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less7 May 2026
- 2026The Newton-Muon Optimizer1 Apr 2026
- 2026Improved Convergence Rates of Muon Optimizer for Nonconvex Optimization27 Jan 2026