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Training models to map natural language instructions to programs given targetworld supervision only requires searching for good programs at training time.Search is commonly done using beam search in the space of partial programs orprogram trees, but as the length of the instructions grows finding a goodprogram becomes difficult. In this work, we propose a search algorithm thatuses the target world state, known at training time, to train a critic networkthat predicts the expected reward of every search state. We then score searchstates on the beam by interpolating their expected reward with the likelihoodof programs represented by the search state. Moreover, we search not in thespace of programs but in a more compressed state of program executions,augmented with recent entities and actions. On the SCONE dataset, we show thatour algorithm dramatically improves performance on all three domains comparedto standard beam search and other baselines.

Value-based Search in Execution Space for Mapping Instructions to Programs