Arnav Kumar Jain Gonzalo Gonzalez-Pumariega Wayne Chen Alexander M Rush Wenting Zhao Sanjiban Choudhury

Abstract

We address the problem of code generation from multi-turn execution feedback. Existing methods either generate code without feedback or use complex, hierarchical reinforcement learning to optimize multi-turn rewards. We propose a simple yet scalable approach, CODE, that solves multi-turn code generation using only single-step rewards. Our key insight is that code generation is a one-step recoverable MDP, where the correct code can be recovered from any intermediate code state in a single turn. CODE iteratively trains both a generator to provide code solutions conditioned on multi-turn execution feedback and a verifier to score the newly generated code. Experimental evaluations show that our approach achieves significant improvements over state-of-the-art baselines. We provide analysis of the design choices of the reward models and policy, and show the efficacy of CODE at utilizing the execution feedback.