Learning Rate - Page 3

The learning rate controls how far a model’s parameters move during each optimization update. If it is too high, training may jump past useful solutions, oscillate, or become unstable; if it is too low, progress can be extremely slow or settle in a poor region. Many training runs start with a warmup period, follow a schedule that reduces the rate over time, or use adaptive optimizers that assign different effective rates to different parameters. The best setting depends on batch size, model architecture, data, and objective. Teams track loss curves and validation performance because a rate that reduces training error quickly may still harm generalization on unseen examples.