What is a likely cause of a spike in loss or oscillations in loss during training?

Setting the learning rate too high can lead to significant fluctuations in the loss or spikes during training. When the learning rate is elevated, the updates to the model parameters can become excessively large, causing the optimization process to overshoot the minimum of the loss function. This results in an unstable training process, where the loss may oscillate without converging or might even increase instead of decrease. Consequently, the learning algorithm struggles to find a stable direction to minimize the error, leading to inefficient learning and a potential failure to achieve optimized performance.

In contrast, factors like insufficient training data, overfitting, or inadequate model architecture have different impacts on the training dynamics and may not directly cause immediate spikes or oscillations in loss. Insufficient training data often leads to underfitting or inability to generalize, while overfitting manifests as improved training loss but deteriorating validation loss. Inadequate model architecture could hinder the model's ability to learn properly but doesn't inherently lead to loss fluctuations akin to an excessively high learning rate.