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Search is an important technique in program synthesis that allows foradaptive strategies such as focusing on particular search directions based onexecution results. Several prior works have demonstrated that neural models areeffective at guiding program synthesis searches. However, a common drawback ofthose approaches is the inability to handle iterative loops, higher-orderfunctions, or lambda functions, thus limiting prior neural searches fromsynthesizing longer and more general programs. We address this gap by designinga search algorithm called LambdaBeam that can construct arbitrary lambdafunctions that compose operations within a given DSL. We create semantic vectorrepresentations of the execution behavior of the lambda functions and train aneural policy network to choose which lambdas to construct during search, andpass them as arguments to higher-order functions to perform loopingcomputations. Our experiments show that LambdaBeam outperforms neural,symbolic, and LLM-based techniques in an integer list manipulation domain.

LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas