Loss function in Faster-RCNN

Question

I read many articles online today about fast R-CNN and faster R-CNN. From which i understand, in faster-RCNN, we train a RPN network to choose "the best region proposals", a thing fast-RCNN does in a non learning way. We have a L1 smooth loss and a log loss in this case to better train the network parameters during backpropagation. Now, i would like to understand a point regarding RPN:

If ,given the region proposal, we had 2 possible (weird case) different objects in the original image, with two different related bounding boxes (both with IoU > 0.7), should we use in the loss function that ground-truth bounding box that has the highest IoU with the predicted anchor box?

Thanks.