Validation loss diverging away from the training loss I used the XLNET for a sentiment classifier in determining whether a comment is positive or negative. I was able to get good results

But when I plotted the validation and training losses I saw this

I think this means that the model is overfitting? But I am not exactly sure. If there is any suggestions I would really appreciate it.
This kind of overfitting is typical when finetuning large LMs.
The usual approaches to &quot;avoid&quot; it are:

Early stopping: select the checkpoint with the best validation loss.
Random restarts: train multiple times from scratch, and select the model with the best validation performance.