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We consider the problem of generating automatic code given sampleinput-output pairs. We train a neural network to map from the current state andthe outputs to the program's next statement. The neural network optimizesmultiple tasks concurrently: the next operation out of a set of high levelcommands, the operands of the next statement, and which variables can bedropped from memory. Using our method we are able to create programs that aremore than twice as long as existing state-of-the-art solutions, while improvingthe success rate for comparable lengths, and cutting the run-time by two ordersof magnitude. Our code, including an implementation of various literaturebaselines, is publicly available at https://github.com/amitz25/PCCoder

Automatic Program Synthesis of Long Programs with a Learned Garbage Collector