arXiv:2605.15530v2 Announce Type: replace Abstract: Neural networks are typically trained with a single learning rate across all layers. While recent empirical evidence suggests that assigning layer-specific learning rates can accelerate training, a principled understanding of the conditions and mechanisms under which non-uniform learning rates are beneficial remains limited. In this work, we investigate non-uniform learning rates through the lens of Stackelberg optimization. Specifically, we demonstrate that training neural networks with a smaller learning rate for the body layers and a large
This paper leverages recent empirical findings on layer-specific learning rates and applies a theoretical framework (Stackelberg optimization) to provide a principled understanding of their benefits, indicating a maturing research area.
Improved understanding and optimization of neural network training techniques can lead to more efficient and powerful AI models, impacting the resources needed for development and deployment across various applications.
The research moves beyond empirical observation of heterogeneous learning rates to provide a theoretical basis, potentially enabling more systematic and effective design of training algorithms for complex neural networks.
- · AI researchers
- · Deep learning framework developers
- · Companies with large AI model training needs
- · Inefficient AI training methods
More efficient training of large neural network models, potentially reducing compute time and costs.
Faster iteration and development of advanced AI capabilities due to optimized learning processes.
Enhanced accessibility to training large models for organizations with limited compute resources, potentially broadening participation in AI development.
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Read at arXiv cs.LG