Making Neural Programming Architectures Generalize via Recursion
Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability. Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity. In order to address these issues, we propose augmenting neural architectures with a key abstraction: recursion. As an application, we implement recursion in the Neural Programmer-Interpreter framework on four tasks: grade-school addition, bubble sort, topological sort, and quicksort. We demonstrate superior generalizability and interpretability with small amounts of training data. Recursion divides the problem into smaller pieces and drastically reduces the domain of each neural network component, making it tractable to prove guarantees about the overall system’s behavior. Our experience suggests that in order for neural architectures to robustly learn program semantics, it is necessary to incorporate a concept like recursion.
This paper improves significantly upon the original NPI work, showing that the model generalizes far better when trained on traces in recursive form. The authors show better sample complexity and generalization results for addition and bubblesort programs, and add two new and more interesting tasks - topological sort and quicksort (added based on reviewer discussion). Furthermore, they actually *prove* that the algorithms learned by the model generalize perfectly, which to my knowledge is the first time this has been done in neural program induction.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Making Neural Programming Architectures Generalize via Recursion
Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability. Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity. In order to address these issues, we propose augmenting neural architectures with a key abstraction: recursion. As an application, we implement recursion in the Neural Programmer-Interpreter framework on four tasks: grade-school addition, bubble sort, topological sort, and quicksort. We demonstrate superior generalizability and interpretability with small amounts of training data. Recursion divides the problem into smaller pieces and drastically reduces the domain of each neural network component, making it tractable to prove guarantees about the overall system’s behavior. Our experience suggests that in order for neural architectures to robustly learn program semantics, it is necessary to incorporate a concept like recursion.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper improves significantly upon the original NPI work, showing that the model generalizes far better when trained on traces in recursive form. The authors show better sample complexity and generalization results for addition and bubblesort programs, and add two new and more interesting tasks - topological sort and quicksort (added based on reviewer discussion). Furthermore, they actually *prove* that the algorithms learned by the model generalize perfectly, which to my knowledge is the first time this has been done in neural program induction.
assistant
This paper improves significantly upon the original NPI work, showing that the model generalizes far better when trained on traces in recursive form. The authors show better sample complexity and generalization results for addition and bubblesort programs, and add two new and more interesting tasks - topological sort and quicksort (added based on reviewer discussion). Furthermore, they actually *prove* that the algorithms learned by the model generalize perfectly, which to my knowledge is the first time this has been done in neural program induction.