Review for NeurIPS paper: Synthesizing Tasks for Block-based Programming

Additional Feedback: The main concern with this work is its relevance to NeurIPS. The application is clearly relevant, as it combines program synthesis, constraint satisfaction, and intelligent tutoring, all well-established in the AI literature. However, the solution is almost entirely symbolic – it combines SMT solving, symbolic execution, and delta debugging over programs. The only probabilistic component is MCTS, and even there (as far as I understand) it is vanilla MCTS without learned policies. The paper has many interesting contributions, but none are related to machine learning.