Recent advances in program synthesis have shown success with methods that employ deep learning on synthetic data generated from domain specific languages (DSLs). In this work, we propose an algorithm for program synthesis that extends these methods. It uses transfer learning from pre-trained language models, and employs a policy improvement operator based on policy-guided search. This hybrid approach combats the challenges of searching a large language space with sparse rewards. We show its effe…