Deep symbolic superoptimization refers to the task of applying deep learning methods to simplify symbolic expressions. Existing approaches either perform supervised training on human-constructed datasets that defines equivalent expression pairs or apply reinforcement learning with human-defined equivalent transformation actions. In short, almost all existing methods rely on human knowledge to define equivalence, which suffers from large labeling cost and learning bias, because it is almost impossible to define and comprehensive equivalent set. We thus propose HISS, a reinforcement learning framework for symbolic superoptimization that keeps humans outside the loop. HISS introduces a tree-LSTM encoder-decoder network with attention to ensure tractable learning. Our experiments show that HISS can discover more simplification rules than existing human-dependent methods, and can learn meaningful embeddings for symbolic expressions, which are indicative of equivalence.
This paper provides a novel approach to the problem of simplifying symbolic expressions without relying on human input and information. To achieve this, they apply a REINFORCE framework with a reward function involving the number of symbols in the final output together with a probabilistic testing scheme to determine equivalence. The model itself consists of a tree-LSTM-based encoder-decoder module with attention, together with a sub tree selector. Their main contribution is that this framework works entirely independent of human-labelled data. In their experiments, they show that this deep learning approach outperforms their provided human-independent baselines, while sharing similar performance with human-dependent ones.
Overall, the work in this paper has the potential to be a contribution to ICLR but lacks completeness and clarity. A number of experimental details are missing, making it difficult to understand the setup under which results were obtained. Moreover, the paper does not seem to have been revised with many grammatical issues that make it hard to read.
The following are major issues within the paper and should be addressed:
•	The paper does not mention the amount of compute given to their model, nor the amount of time taken to train. As the REINFORCE framework is generally quite computation-heavy, these are significant details. Without assessing the amount of compute and time allotted for training HISS, the comparisons to previous baselines lose a fair amount of meaning. The paper alludes to processes being ‘extremely time consuming’, but then does not provide any numbers.
•	They do not mention the data used to train the model weights. In the comparisons sections, some details on datasets are given, but these seem to refer to data for inference.
•	There are many grammatical errors that likely could have been detected with 	 revision. A handful of such errors would not affect the score, but they are so numerous as to make the paper much more difficult to understand.
Additionally, these are comments that slightly detract from the quality of the paper:
•	It’s unclear what to glean from Section 5.1, as the dataset and baselines seem to be fairly trivial. If their claim is to have the first nontrivial human-independent approach to simplifying symbolic expressions, there is no need to compare to baselines that can only handle small expressions.
•	Sections 5.3 and 5.4 contribute little to the paper. For 5.3, the model was trained to embed equivalent expressions close together, using L2 regularization. It is therefore unsurprising that equivalent expressions are then closer together than non-equivalent ones. The paper also does not provide a comparison to the method without this regularization, and so it’s unclear if this embedding similarity helps in any way.  For 5.4, the section is extremely short and contains very little content. Moreover, just as many of the variables in their provided examples oppose their conjectures as support them.
•	The most interesting figure provided is the rewrite rules discovered by the model. It would be even better if an additional column containing the rules discovered by Halide (the main baseline) were provided.
Overall, in my understanding, the primary point in favor of the paper is in being the first nontrivial human-independent approach to simplifying symbolic expression. That said, this is not my area of expertise, so I cannot judge novelty or importance as well as other reviewers.