Iterative Self-training for Code Generation via Reinforced Re-ranking

Nikita Sorokin, Ivan Sedykh, Valentin Malykh

Abstract: Generating high-quality code that solves complex programming tasks is challenging, especially with current decoder-based models that produce highly stochastic outputs. In code generation, even minor errors can easily break the entire solution. Leveraging multiple sampled solutions can significantly improve the overall output quality.