Making sense of loss and accuracy curves
Just a couple of points below

Red box: In strict terms, overfitting takes place when the model is doing better in the training than the validation set. And so, I would say here it starts earlier than what is denoted in red and it probably does relate to the model finding better parameters that only apply to the training set. Of note, no significant train loss decrease.
Green box: In training, significant drop in the loss. This discovery of a significantly better minima means it may also apply in the validation set and it is actually reflected in the val loss curve. Despite that, the loss is no better than the checkpoint at the beginning of the red box.
Blue box: Loss and accuracy are not co-dependent metrics. Whereas accuracy is a measure of the number of correct guesses (how many), the loss is a measure of distance from correct given a data point (how far off).

Generally, smaller LR means the model has less &quot;freedom&quot; to hop very far around in the feature space. And so there are fewer chances to see significant and fast loss decrease. As you suggest, there is a slight upward overfitting trend in your val loss but is as significant or fast as the smaller LR allows in training.
Also, note that your train loss is higher than val loss for a good amount of your training (~55th epoch). You may want to investigate how your model is regularised as this may affect your learning curves in ways that may help you infer diagnostics better. E.g. in PyTorch, train loss between model states (model.train() and model.eval()) differ significantly.