  Many approaches to program synthesis perform a search within an enormous
space of programs to find one that satisfies a given specification. Prior works
have used neural models to guide combinatorial search algorithms, but such
approaches still explore a huge portion of the search space and quickly become
intractable as the size of the desired program increases. To tame the search
space blowup, we propose training a neural model to learn a hands-on search
policy for bottom-up synthesis, instead of relying on a combinatorial search
algorithm. Our approach, called CrossBeam, uses the neural model to choose how
to combine previously-explored programs into new programs, taking into account
the search history and partial program executions. Motivated by work in
structured prediction on learning to search, CrossBeam is trained on-policy
using data extracted from its own bottom-up searches on training tasks. We
evaluate CrossBeam in two very different domains, string manipulation and logic
programming. We observe that CrossBeam learns to search efficiently, exploring
much smaller portions of the program space compared to the state-of-the-art.
