CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Understand

Program synthesis or code generation aims to generate a program that satisfies a problem specification.

• Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations.
• In particular, they often follow a standard supervised fine-tuning procedure to train a code generation model only from the pairs of natural-language problem descriptions and ground-truth programs.
• Such paradigm largely ignores some important but potentially useful signals in the problem specification such as unit tests, which thus often results in poor performance when solving complex unseen coding tasks.