text: The approach solves an important problem  as getting labelled data is hard.  The focus is on the key aspect, which is generalisation across heteregeneous data.  The novel idea is the dataset embedding  so that their RL policy can be trained to work across diverse datasets. Pros: 1. The approach performs well against all the baselines, and also achieves good cross-task generalisation in the tasks they evaluated on.  2. In particular, they alsoevaluated on test datasets with fairly different statistics from the training datasets, which isnt very common in most meta-learning papers today,  so it’s encouraging that the method works in that regime. Cons: 1. The embedding strategy, especially the representative and discriminative histograms, is complicated.  It is unclear if the strategy is general enough to work on harder problems / larger datasets, or with higher dimensional data like images.  More evidence in the paper for why it would work on harder problems would be great.  2. The policy network would have to output a probability for each datapoint in the dataset U,  which could be fairly large,  thus the method is computationally much more expensive than random sampling.  A section devoted to showing what practical problems could be potentially solved by this method would be useful. 3. It is unclear to me if the results in table 3 and 4 are achieved by retraining from scratch with an RBF SVM, or by freezing the policy network trained on a linear SVM and directly evaluating it with a RBF SVM base learner. Significance/Conclusion: The idea of meta-learning or learning to learn is fairly common now.  While they do show good performance,  it’s unclear if the specific embedding strategy suggested in this paper will generalise to harder tasks.  Comments: There’s lots of typos,  please proof read to improve the paper.