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.

为了解决程序综合中搜索空间膨胀的问题，我们提出了 CrossBeam，它训练一个神经模型来学习自底向上的搜索策略，从而更加高效地探索程序空间。我们在字符串操作和逻辑编程领域实验后发现，CrossBeam 学会了高效搜索，相比最先进的技术，它探索了更小的程序空间。

CrossBeam: 底部向上程序综合中的搜索学习