I am presuming that state is the c value that one node carries forward to another node in the LSTM model. If data is properly normalized then the state must be generated out of proper regression and should be helpful in the next batch too. Personally, I have always found model converging soon without resetting the state. Can someone point out some example where the resetting state would have converged model sooner or more accurate than not resetting it?
