text: SUMMARY: This work is about learning the validity of a sequences in specific application domains like SMILES strings for chemical compounds.  In particular, the main emphasis is on predicting if a prefix sequence could possibly be extended to a complete valid sequence.  In other words, one tries to predict if there exists a valid suffix sequence, and based on these predictions, the goal is to train a generative model that always produces valid sequences.   In the proposed reinforcement learning setting, a neural network models the probability that a certain action (adding a symbol) will result in a valid full sequence.  For training the network, a large set of (validity-)labelled sequences would be needed.  To overcome this problem, the authors introduce an active learning strategy, where the information gain is re-expressed as the conditional mutual information between the the label y and the network weights w, and this mutual information is maximized in a greedy sequential manner.     EVALUATION: CLARITY & NOVELTY: In principle, the paper is easy to read.  Unfortunately, however, for the reader is is not easy to find out what the authors consider their most relevant contribution.  Every single part of the model seems to be quite standard (basically a network that predicts the probability of a valid sequence and an information-gain based active learning strategy)  - so is the specific application to SMILES strings what makes the difference here?    Or is is the specific greedy approximation to the mutual information criterion in the active learning part?  Or is it the way how you augment the dataset?  All these aspects might be interesting,  but somehow I am missing a coherent picture. SIGNIFICANCE: it is not entirely clear to me if the proposed "pruning" strategy for the completion of prefix sequences can indeed be generally applied to sequence modelling problems,  because in more general domains it might be very difficult to come up with reasonable validity estimates for prefixes that are significantly shorter than the whole sequence.  I am not so familiar with SMILES strings  -- but could it be that the experimental success reported here is mainly a result of the very specific structure of valid SMILES strings?   But then, what can be learned for general sequence validation problems?