arXiv:2605.21104v1 Announce Type: new Abstract: Sparsifying transformers remains a fundamental challenge, as standard optimizers fail to simultaneously encourage sparsity and maintain training stability. Effective adaptive optimizers exhibit an implicit $L_{\infty}$ bias favoring stability, yet, sparsity requires an $L_1$ bias. To integrate sparsity, we propose a composition of optimizer steps, which we cast as non-commutative operators to analyze and combine their optimization geometry in a principled way. This yields HORST (Hyperbolic Operator for Robust Sparse Training), a modular optimizer
The continuous push for more efficient and scalable AI models necessitates innovations in training methodologies, especially as model sizes grow.
This development addresses a fundamental challenge in optimizing large-scale AI models, potentially making sparse transformers more practical and widely applicable across various AI applications.
The ability to stably train sparse transformers with HORST means more efficient and less resource-intensive AI models could become standard, impacting hardware requirements and computational costs.
- · AI researchers and developers
- · Cloud computing providers (reduced cost)
- · Companies deploying large language models
- · Hardware manufacturers (new optimization targets)
- · Inefficient AI training methods
- · Companies solely reliant on dense model architectures
More widespread adoption of sparse transformer models due to improved training stability and efficiency.
Reduced computational resource demand for training advanced AI, potentially lowering the barrier to entry for smaller organizations.
Acceleration of AI development in areas currently bottlenecked by the immense computational cost of dense model training.
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Read at arXiv cs.LG