Dynamic Learning Rate Scheduling based on Loss Changes Leads to Faster Convergence

This research introduces a dynamic learning rate scheduling method based on loss changes, aiming to achieve faster convergence in machine learning models, offering practical benefits for optimizing training efficiency and model performance across various AI tasks.