This is expected, as you would use the  state_dict  to initialize a model, thus these tensors don’t store any Autograd history.

Would these tensors then have their requires_grad flag set to True after calling Model.train() prior to starting the training loop for the main model? It seems that calling Model.train() would set the requires_grad flag for all the parameters to True.
I’ve seen the autograd mechanics documentation as well as several other posts regarding freezing layer weights but all of them only mention either setting the requires_grad flag for the parameters that you wish to freeze to False, or inputting only the parameters that you wish to train into your optimizer instance.