Gradient descent at the Edge of Stability: free energy model and kinetic description of the two-layer network
arXiv:2606.05326v1 Announce Type: cross Abstract: We study the dynamics of gradient descent in the Edge of Stability regime, where the learning rate is large enough to induce persistent oscillations in the loss and the sharpness. We propose a continuous-time effective model that tracks the evolution of the average trajectory coupled with the time-averaged covariance of its fast oscillations. Our analysis reveals that the natural quantity to monitor in such unstable regimes is an effective free energy, which combines the original risk functional with a curvature-related "entropic" term. Our mod
The continuous push for more efficient and robust AI training methodologies, particularly as models grow in complexity, highlights the need to understand fundamental dynamics like the Edge of Stability.
Understanding the 'Edge of Stability' regime in gradient descent is critical for optimizing AI training, potentially leading to faster convergence, better generalization, and more stable large-scale model development.
This research provides a theoretical framework and kinetic description for understanding complex AI training dynamics, offering new avenues for developing more effective and predictable optimization algorithms.
- · AI researchers
- · Deep learning developers
- · High-performance computing providers
- · AI model operators
Improved understanding of deep learning optimization leads to more advanced and stable AI models.
This refined understanding could enable more efficient use of compute resources, reducing training times and costs for complex AI systems.
Advances in training stability could accelerate the development and deployment of sophisticated AI agents across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG