Since you posted in nlp, I assume your input data is text. I would also assume that your train a classification task, because in this case I’ve also seen a big drop of the training loss after the first epoch. I think(!) this is even more pronounced when using pretrained word embeddings.
The first plot definitely looks “as usual”: the training loss goes down towards overfitting, while the validation loss starts to increase due to that overfitting. The absolute values depend on how exactly you calculate the loss, so they don’t really help.
In the second plot, I’m not sure about the jumps at epoch 17/18 and 32/33. They look rather regular. Do you reset the learning rate or something? The difference in absolute numbers between the 2 plots may stem from the different datasets. I assume, that for the first plot, the training and validation data came from the same dataset. Where do the 750 other samples come from? In any case, 750 is not a large training set – neither is 4,500 – so the overlap in the distribution of the training in validation data might be large.
In short, I don’t see any inherent problems with your graphs, and I can easily explain what I see (maybe part from the jumps). Of course, that doesn’t mean I’m right :).