arXiv:2606.25975v1 Announce Type: new Abstract: Common first-order optimizers, such as Adam, implicitly treat each parameter block as an unstructured vector, which disregards the multilinear weight structure present in many modern machine learning models. Recent work has shown that exploiting matrix structure can improve optimization dynamics. A notable example is Muon, which performs steepest descent under the spectral norm constraint. We take the next step and introduce Tensorion, a tensor-aware optimizer that extends Muon's constrained optimization perspective from matrices to higher-order
The continuous drive for more efficient and powerful AI models necessitates innovations in optimization algorithms as current methods reach their limits with increasingly complex architectures.
This development can significantly improve the training efficiency and performance of advanced machine learning models, leading to faster research cycles and more capable AI systems.
Optimization of complex AI models could become significantly more efficient, potentially enabling the training of larger, more sophisticated models with existing compute resources.
- · AI researchers and developers
- · Companies building large AI models
- · High-performance computing providers
- · AI-powered product companies
- · Developers reliant on less efficient optimizers
- · Companies with suboptimal AI training pipelines
Tensorion could lead to faster convergence and better generalization in, for example, transformer-based models and multi-modal AI systems.
Improved optimization techniques could reduce the computational requirements for developing new AI models, lowering barriers to entry for some research and development.
This could accelerate the development of more advanced AI agents and autonomous systems by making their training more feasible.
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Read at arXiv cs.LG