arXiv:2511.04981v2 Announce Type: replace Abstract: Model depth is a double-edged sword in deep learning: deeper models achieve higher accuracy but require higher computational cost. To efficiently train models at scale, progressive training (also known as model expansion) scales up model capacity during training and significantly reduces computation with little performance degradation. In this work, we study the depth expansion of large-scale models through the lens of optimization theory and feature learning, offering insights on the initialization of new layers, hyperparameter transfer, lea
The continuous drive for more performant AI models necessitates innovative solutions for efficient scaling, making research into depth expansion timely.
This research provides a pathway to achieve higher accuracy in deep learning models while mitigating the accompanying computational cost, a critical constraint for AI development.
The efficiency of scaling deep learning models, particularly concerning their depth, could significantly improve, leading to more powerful and accessible AI.
- · AI researchers and developers
- · Cloud providers
- · Companies with large AI training needs
- · Inefficient AI training methodologies
- · GPU manufacturers if efficiency gains significantly outpace demand growth
More computationally efficient training of large-scale deep learning models.
Accelerated development and deployment of complex AI systems across various industries.
Potentially democratized access to advanced AI capabilities due to reduced resource requirements.
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