Did you restart at 3300’th iteration? Or did you run it all along?. I think you need to give more info. what is the problem you are working with? What’s the step-size?
In anycase, the problem with Adam is that it uses moving average in the denominator term. So if the gradients get really small and the whole of denominator will be small. Since the gradients are already small, the denominator results in blowup thus pushing you very far away hence huge loss.  You may have a look at https://openreview.net/forum?id=ryQu7f-RZ 450 . I think there are many recent methods which avert this problem including AMSGrad (in the earlier mentioned paper), Hyper-gradient descent (for Adam) etc. Also look for comments in the openreview forum, there seems to be further discussion on this issue. Unfortunately i do not know of any pytorch implementation of these algorithms.