Linear warm-up is a training strategy that gradually increases the learning rate from a small initial value to a target learning rate over a predefined number of steps or epochs. This approach helps stabilize the training process by allowing the model to adapt to the optimization landscape without making drastic updates early in training, which can lead to better convergence and improved performance.
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