arXiv:2512.04632v2 Announce Type: replace Abstract: Orthogonality-based optimizers, such as Muon, have recently shown strong performance across large-scale training and community-driven efficiency challenges. However, these methods rely on a costly gradient orthogonalization step. Even efficient iterative approximations such as Newton-Schulz remain expensive, typically requiring dozens of matrix multiplications to converge. We introduce a pre-conditioning procedure that improves the initialization of the Newton--Schulz iterations while incurring negligible overhead. Furthermore, our pre-condit
The continuous advancements in AI model complexity necessitate more efficient optimization techniques to meet current training demands and reduce computational costs.
Improved optimization algorithms directly impact the speed and cost of training large AI models, accelerating research and deployment across various AI applications.
This pre-conditioning procedure reduces the computational burden of orthogonality-based optimizers, potentially making complex AI training more accessible and faster.
- · AI researchers
- · Large language model developers
- · Cloud AI providers
- · Chip manufacturers (indirectly through increased demand)
- · Inefficient optimization techniques
Faster and more cost-effective training of advanced AI models becomes possible.
This could lead to a quicker iteration cycle for AI model development and deployment in industry.
Increased efficiency might lower barriers to entry for advanced AI development, fostering broader innovation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI