Characterizing Optimizer-Dependent Training Dynamics Through Hessian Eigenvector Displacement and Localization
arXiv:2606.30226v1 Announce Type: new Abstract: Hessian spectral properties are a standard tool in analysing neural-network training, with eigenvalues linked to sharpness, generalization, and optimization dynamics. Eigenvalues quantify curvature magnitude, while eigenvectors identify which parameters generate that curvature. In this work, we study how the leading Hessian eigenvectors evolve during training and how they affect the learning trajectories. We track the training dynamics of multilayer perceptrons on a classification problem and measure eigenvector dynamics through two complementary
This research provides deeper insight into the fundamental training dynamics of neural networks, coinciding with current efforts to improve AI efficiency and understanding.
Understanding how optimizers influence training dynamics through Hessian eigenvectors can lead to more efficient, stable, and generalizable AI models, impacting the development trajectory of advanced AI systems.
Current understanding of AI training optimization becomes more granular, potentially enabling researchers to design better algorithms or diagnose training issues more effectively.
- · AI researchers
- · Deep learning practitioners
- · AI model developers
- · Compute infrastructure providers
- · Developers using inefficient optimization techniques
Improved understanding of AI training leads to more robust and performant models.
Faster convergence and better generalization allow for the development of more complex and capable AI applications.
The enhanced efficiency in AI development could accelerate the overall progress towards advanced AI capabilities, touching various sectors.
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Read at arXiv cs.LG