Noise-Driven Exploration and Transient Freezing Select Flat Minima in Stochastic Gradient Descent
arXiv:2601.10962v2 Announce Type: replace Abstract: Stochastic gradient descent (SGD) is central to deep learning, yet the dynamical origin of its preference for flatter, more generalizable solutions remains unclear. Here, by analyzing SGD learning dynamics, we identify a nonequilibrium mechanism that governs solution selection during training. Numerical experiments reveal a transient exploratory phase in which SGD trajectories repeatedly escape sharp valleys and migrate toward flatter regions of the loss landscape before becoming confined to a final basin. Using a tractable physical model, we
This research provides a deeper theoretical understanding of SGD, a fundamental AI training algorithm, by explaining its preference for flatter, more generalizable solutions, which has been an open question.
Understanding the mechanisms behind SGD's effectiveness can lead to more robust, efficient, and reliable AI models, impacting the development and deployment of advanced AI systems.
This research enhances foundational knowledge in AI optimization, potentially informing future algorithm design for improved model generalization and stability.
- · AI researchers
- · Deep learning practitioners
- · AI model developers
Improved theoretical understanding of deep learning optimization provides insights into model behavior.
New optimization algorithms emerge, leveraging these insights to train more efficient and reliable AI models.
The development of more explainable and trustworthy AI systems, as the underlying training dynamics are better understood.
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