When training a model, which is more important, minimizing training loss or maximizing test accuracy?


While training the model, I have some question about the training loss.

This plot is the result of training with different A and B optimizers.
The difference between the training loss values of A and B is large, but the test accuracy is almost the same, or A has higher accuracy.
If so, does the training loss have no significant relationship with the test accuracy?
In my opinion, from a traditional optimizer perspective, an optimizer that minimizes training loss is good.
What do you think about this issue?