Proud to announce our paper on "Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis" has been accepted to Findings of #EMNLP2023 .
This is joint work with Matthieu Zimmer, Gerasimos Lampouras, Derrick Goh Xin Deik, and Ignacio Iacobacci .

Code Synthesis, the generation of programming language code from a natural language description, is a challenging problem for #LLMs.
Various Reinforcement Learning methods have been proposed to improve performance of pretrained models.
One #RL approach to this problem is to use functional tests (Unit Tests) as the reward signal; however, this requires data consisting of (i) NL problem prompts, (ii) varied unit tests for each problem to assess functional correctness, which is often unavaible. Some datatasets such as #HumanEval and #MBPP exist; however, these are limited in size and contain (relatively) simple problems.

We show how to programmatically derive new training data for functional test-based Code Synthesis RL, generating and converting automatic tests from a strongly typed language (Java) to a weakly typed language (Python). This allows us to generate arbitrary amounts of test-annotated data.

We then introduce a very straight-forward yet effective practical REINFORCE-based Actor-Critic RL approach that makes use of Unit Test annotated data to tune a function-level Code Synthesis LM.
Crucially, we find that keeping the Critic in sync with the Policy yields better results than pretraining and freezing the Critic.
Use of our augmentation data further improves model performance.

Preprint available at https://arxiv.org/abs/2310.13669 ; code and model will be made available.

#Machinelearning #AI #ML #ReinforcementLearning #LLM #PLM #CodeSyntheis #Huawei