Efficient Distributed Orthonormal Optimizers for Large-Scale Training
Kwangjun Ahn from Microsoft Research provides a technical overview of orthonormal optimizers (like Muon and Dion2), a new class of algorithms for large-scale AI model training that are emerging as powerful successors to AdamW. The talk covers their theoretical foundations, empirical benefits, distributed implementation strategies, and practical guidelines for integration into modern training pipelines.
