What is a potential consequence of using a very high learning rate?

Using a very high learning rate can indeed lead to an unstable training process. When the learning rate is too high, the model updates its weights too aggressively during training, causing the optimization process to overshoot the minimum of the loss function. Instead of gradually converging towards the optimal solution, the loss may fluctuate wildly or even diverge. This instability can prevent the model from learning effectively, resulting in poor performance and potentially causing the training to fail altogether.

In contrast, a balanced learning rate is crucial for ensuring that the model converges smoothly towards the optimal weights, allowing for fine-tuning and adjustments as needed. This stability is fundamental to effective training, making careful selection of the learning rate an essential part of the machine learning process.