What may result from choosing a small learning rate during model training?

Choosing a small learning rate during model training can indeed lead to longer training times. A smaller learning rate means that the model updates its weights slowly in response to the gradient of the loss function. While this can help in achieving more precise updates and avoiding overshooting, it typically results in a greater number of iterations needed to reach convergence.

As the model takes smaller steps toward optimizing the loss function, it works its way toward the optimal parameters more gradually. This slower pace can be beneficial in some scenarios, but it also means the training process will extend over a longer time period before the model achieves its desired accuracy. In practical terms, you might need to run the training for more epochs or iterations to see significant improvements.