Large Language Models for Code (Code LLM)  are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task.
Various approaches have been proposed to boost the code generation performance of pre-trained Code LLMs, such as supervised fine-tuning, instruction tuning, reinforcement learning, etc.
In this paper, we propose a novelRRTF(RankResponses to alignTest&TeacherFeedback)  framework, which can effectively and efficiently boost pre-trained large language models for code generation. Under this framework, we present PanGu-Coder2, which achieves 62.20% pass@1 on the OpenAI HumanEval benchmark.
Furthermore, through an extensive evaluation on CoderEval and LeetCode benchmarks, we show that PanGu-Coder2 consistently outperforms all previous Code LLMs.