arXiv:2510.03164v2 Announce Type: replace Abstract: Learning rate warm-up -- increasing the learning rate at the beginning of training -- has become a ubiquitous heuristic in modern deep learning, yet its theoretical foundations remain poorly understood. In this work, we provide a principled explanation for why warm-up improves training. We rely on a generalization of the $(L_0, L_1)$-smoothness condition, which bounds local curvature as a linear function of the loss suboptimality and exhibits desirable closure properties. We show -- both theoretically and empirically -- that this condition is
The paper provides a theoretical explanation for a widely adopted empirical technique in deep learning, addressing a long-standing gap in understanding 'warm-up' schedules.
Improved theoretical understanding of deep learning training mechanisms can lead to more robust, efficient, and predictable AI model development, impacting research and applications.
This theoretical grounding provides a basis for optimizing deep learning algorithms more effectively, potentially reducing computational costs and improving model performance.
- · AI researchers
- · Deep learning practitioners
- · Companies developing AI models
This research provides a foundational understanding for an empirical deep learning technique.
It could inspire new algorithms or more optimized training schedules, leading to faster or more effective AI model development.
Deeper theoretical insights might unlock capabilities for more complex or data-efficient AI, impacting future AI systems and their application across sectors.
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Read at arXiv cs.LG