If you want to do it this way, I would just leave out the duplicate parameters in one of the step functions.
But in general, it makes me wonder what you intend to gain over summing the loss. If you do the backprop and step separately and keep the grads for both this means you get the adaptivity of the optimizer (if there is any, e.g. because you use Adam or so) tailored to either loss.
But even then: In the end, you optimize some loss function and you likely implicitly optimize a combination of the two losses, so I wonder what there is to gain to make this implicit rather than explicit.
All that said, you can run the experiments, if the result works better for you, reality beats my limited intuition.
Best regards
Thomas