C t is called a candidate hidden state. Reasonably, hidden states at t = 0 are initialized with zeros so that gates and states at t = 1 process sequences with no past information. However, noisy initialization may be practical as well since the network processing subsequences of arbitrary length will not adapt to the zero state. Apart from initialization, we should also decide on how to reset states every training iteration. Intuitively for column sampling, we will keep the previous state for a new minibatch until we encounter the minibatch with the final column, i.e. the subsequences ending with end-of-SMILES or padding tokens. For consecutive sampling, we might want to retain or even train the state throughout an epoch. Random sampling will likely fit with noisy state reinitialization. Lastly, depending on the preferred sampling technique, we will detach hidden states from the computational graph (truncated BPTT ).