Reviews: Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction

Summary A method for learning a DSL for program synthesis together with a search algorithm in that DSL is presented. The method proceeds iteratively, trying to solve tasks with the current DSL, and then extracting new DSL components from the solutions. Experiments show that bootstrapping the method with a DSL made up of trivial primitives is sufficient to discover common high-level constructs present in manually constructed DSLs. The paper tackles an important problem (DSL design) in an elegant and novel way. The clarity of the paper is not perfect, as the details of the idea require more space than the 8 pages available, but it clearly is stepping stone towards a new generation of program synthesis approaches.