Mini toolsDefinition
An initial training phase where the learning rate gradually increases from a small value. This helps stabilize early training in deep neural networks.
Detailed Explanation
Warmup steps involve gradually increasing the learning rate from a very small value to the intended initial learning rate over a specified number of training steps. This technique helps establish good initial parameter values and prevents unstable gradient updates early in training. It's often combined with other learning rate scheduling techniques.
Use Cases
Large model training Transformer models Deep neural networks Fine-tuning procedures
