Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
Philip Gorinski, Matthieu Zimmer, Gerasimos Lampouras, Derrick Goh Xin Deik, Ignacio Iacobacci
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.- Anthology ID:
- 2023.findings-emnlp.28
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 370–384
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.28
- DOI:
- 10.18653/v1/2023.findings-emnlp.28
- Cite (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.
- Cite (Informal):
- Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis (Gorinski et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-emnlp.28.pdf