Unstable Convergence

Unstable convergence in machine learning refers to the phenomenon where optimization algorithms, even gradient descent, converge to a solution despite violating standard convergence conditions, often exhibiting oscillations or erratic behavior. Current research focuses on understanding this instability in various contexts, including large language models, sharpness-aware minimization, and neural networks trained with large learning rates, investigating its causes (e.g., saddle points, high dimensionality, non-convexity) and potential benefits (e.g., improved generalization). This research is significant because it challenges established theoretical frameworks and may lead to improved training strategies and a deeper understanding of the dynamics of complex optimization landscapes in machine learning.