paper-with-me

홈 › Papers

Dion3: Full-Stack Orthogonal Updates

2026-08-12 · Noah Amsel, Jack Zhang, Kwangjun Ahn, Ali Naeimi, Austin Feng, Berlin Chen, Tri Dao, John Langford hf

The Muon optimizer incurs a significant overhead cost due to its cubic-time Newton-Schulz orthogonalization step. When weights are sharded, communication overhead compounds this computational cost, eroding the benefits of Muon in many settings. We present Dion3, a revision of Muon that targets this overhead at every level of the stack. Our Gram Newton-Schulz algorithm reduces the FLOP cost of orthogonalization, our CuteDSL kernels accelerate it by exploiting symmetry, and our megabatching strategy reduces communication overhead. Moreover, we propose a simple change to the update rule that cuts costs even further: selecting only a fraction of the momentum matrix's rows to orthogonalize at each step. This update rule improves on Dion (another "compressed" version of Muon), in both speed and performance. Overall, Dion3 matches or improves on the loss achieved by Muon but reduces optimizer step time by up to 6x. Dion3 is available via the dion package (https://github.com/microsoft/dion) as a drop-in replacement for Muon.

📄 PDF Abstract BibTeX arXiv:2608.11612

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimization

2026-05-07 · Tatsuhiro Nakamori, Laura Gomezjurado Gonzalez, Ganesh Talluri, Ansh Tiwari 외 arxiv

Low-rank gradient compression reduces communication in distributed training by representing updates with rank-$r$ factors. Dion is a recent method that approximates Muon, a spectral optimizer that orthogonalizes momentum…

Dion: Distributed Orthonormalized Updates

2025-04-07 · Kwangjun Ahn, Byron Xu, Natalie Abreu, John Langford

Recent work has shown that orthonormal matrix updates speed up neural network optimization, improve training stability, and offer better hyperparameter transfer across model sizes. Applying these updates efficiently when…

SOON: Symmetric Orthogonal Operator Network for Global Subseasonal-to-Seasonal Climate Forecasting

2026-02-04 · Ziyu Zhou, Tian Zhou, Shiyu Wang, James Kwok 외 arxiv

Accurate global Subseasonal-to-Seasonal (S2S) climate forecasting is critical for disaster preparedness and resource management, yet it remains challenging due to chaotic atmospheric dynamics. Existing models predominant…

Computational Efficiency

Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification

2020-10-29 · Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman 외

Distributed model training suffers from communication bottlenecks due to frequent model updates transmitted across compute nodes. To alleviate these bottlenecks, practitioners use gradient compression techniques like spa…

Quantization

CurvaDion: Curvature-Adaptive Distributed Orthonormalization

2025-12-13 · Bhavesh Kumar, Roger Jin, Jeffrey Quesnelle arxiv

As language models scale to trillions of parameters, distributed training across many GPUs becomes essential, yet gradient synchronization over high-bandwidth, low-latency networks remains a critical bottleneck. While re…