Thank you all for your answers. Whereas I agree that the specific padding strategy should not affect the results, in my implementation (actually, implementations, as I am testing alternative codes) the training results do differ. Perhaps not huge differences, but I can still see them (even though I am keeping all the rest fixed, seeds included).
So I was wondering whether my implementations are wrong or perhaps there is the possibility that somehow inputs padded at different lengths can have a small numerical effect on the gradients… 
Thanks again!