Reducing the pace of learning should not increase overfitting.

The rate of learning is calculated by comparing the “contribution” of the most recent set of observations to all prior batches.

The smaller the learning rate, the less significant the most recent batch.

Reducing the learning rate improves the likelihood of convergence, but requires more iterations to find the optimal solution (local, if not global).

In conclusion, the pace of learning influences the convergence rate, but is not overfitting.

- The primary cause of overfitting with NNs is the same as with other Machine Learning models:
**Comparing the complexity of a model (number of parameters) to the amount of training data.**

You may believe that one impacts the other since the number of training epochs is insufficient for the slower learning rate. This may prevent the system from arriving at the ideal solution. Therefore, a low learning rate results in more iterations, and vice versa.

It is also possible that lower step sizes result in the neural network learning a more precise answer, causing overfitting. A modest learning rate in Machine Learning would overshoot such spots – never settling, but bouncing about; hence, it would likely generalize well.

Therefore, the collected information must be connected to the optimizer learning rate, although to a lesser degree.

### Conclusion

A slower rate of learning in a neural network increases the likelihood of being mired in a local minimum. Consequently, your model may become less generic, and it would suffer. I’ve never considered it that way before. Select the maximum learning rate that stabilizes the error in your loss function. In either case, the gradient descent reduces as the product of the constant learning rate and the slope approaches zero, regardless of your learning pace.