How does gradient descent work?

Researchers discovered that gradient descent in deep learning operates fundamentally differently than traditional optimization theory predicts, with neural networks oscillating at the "edge of stability" rather than staying in stable regions. They developed a new "central flow" analysis using third-order Taylor expansions that explains how these oscillations automatically reduce sharpness when it exceeds critical thresholds, allowing the algorithm to regulate itself and continue making progress toward lower loss values.