SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
arXiv:2602.02472v2 Announce Type: replace Abstract: Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansion remains significantly understudied, with the few existing methods limited to the early stages of training. However, expanding width during the mid-stage is essential for maximizing computational savings, yet it remains a formidable challenge due to severe training instabilities. Empirically, we show that naive initialization at this stage disrupts activation stati
The increasing scale of AI models necessitates more efficient training methods, and research into width-progressive learning is a critical next step to optimize computational resources.
Improving the efficiency of AI model training, especially through width expansion, directly addresses the growing computational overhead and energy demands associated with advanced AI development.
This research provides a pathway to meaningfully reduce the computational resources needed for large-scale AI model pre-training, enabling more efficient and potentially more widespread AI development.
- · AI developers
- · Cloud providers
- · AI research institutions
- · Companies with inefficient model training practices
Reduced computational costs and time for training large AI models.
Accelerated AI development and deployment, making advanced AI more accessible.
Potentially democratized access to large-scale AI for a wider range of organizations, fostering more innovation.
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Read at arXiv cs.LG