Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis

• URL: http://arxiv.org/abs/2310.13669v1
• Date: Fri, 20 Oct 2023 17:13:16 GMT
• Title: Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
• Authors: Philip John Gorinski, Matthieu Zimmer, Gerasimos Lampouras, Derrick Goh Xin Deik, Ignacio Iacobacci
• Abstract summary: We present a novel approach to automatically obtain data consisting of function signatures and associated Unit Tests. We show that it, in conjunction with automatically generated training data, leads to improvement of a pre-trained code language model's performance.
• Score: 16.88062487980405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of large pre-trained language models in the domain of Code Synthesis has shown remarkable performance on various benchmarks, treating the problem of Code Generation in a fashion similar to Natural Language Generation, trained with a Language Modelling (LM) objective. In addition, the property of programming language code being precisely evaluable with respect to its semantics -- through the use of Unit Tests to check its functional correctness -- lends itself to using Reinforcement Learning (RL) as a further training paradigm. Previous work has shown that RL can be applied as such to improve models' coding capabilities; however, such RL-based methods rely on a reward signal based on defined Unit Tests, which are much harder to obtain compared to the huge crawled code datasets used in LM objectives. In this work, we present a novel approach to automatically obtain data consisting of function signatures and associated Unit Tests, suitable for RL training of Code Synthesis models. We also introduce a straightforward, simple yet effective Actor-Critic RL training scheme and show that it, in conjunction with automatically generated training data, leads to improvement of a pre-trained code language model's performance by up to 9.9% improvement over the original underlying code synthesis LM, and up to 4.3% over RL-based models trained with standard PPO or CodeRL.

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