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    <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.</abstract>
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%0 Conference Proceedings
%T Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
%A Gorinski, Philip
%A Zimmer, Matthieu
%A Lampouras, Gerasimos
%A Deik, Derrick Goh Xin
%A Iacobacci, Ignacio
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F gorinski-etal-2023-automatic
%X 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.
%R 10.18653/v1/2023.findings-emnlp.28
%U https://aclanthology.org/2023.findings-emnlp.28/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.28
%P 370-384

Markdown (Informal)

[Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis](https://aclanthology.org/2023.findings-emnlp.28/) (Gorinski et al., Findings 2023)

• Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis (Gorinski et al., Findings 2023)

ACL

• Philip Gorinski, Matthieu Zimmer, Gerasimos Lampouras, Derrick Goh Xin Deik, and Ignacio Iacobacci. 2023. Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 370–384, Singapore. Association for Computational Linguistics.