The impressive performance of proprietary LLMs like GPT4 in code generation has led to a trend to replicate these capabilities in open-source models through knowledge distillation (e.g. Code Evol-Instruct). However, these efforts often neglect the crucial aspect of response quality, relying heavily on teacher models for direct response distillation. This paradigm, especially for complex instructions, can degrade the quality of synthesized data, compromising the knowledge distillation process. To this end, our study introduces the Adaptive Modular Response Evolution (AMR-Evol) framework, which employs a two-stage process to refine response distillation. The first stage, modular decomposition, breaks down the direct response into more manageable sub-modules. The second stage, adaptive response evolution, automatically evolves the response with the related function modules. Our experiments with three popular code benchmarks (HumanEval, MBPP, and EvalPlus) attest to the superiority of the AMR-Evol framework over baseline response distillation methods. By comparing with the open-source Code LLMs trained on a similar scale of data, we observed performance enhancements: more than +3.0 points on HumanEval-Plus and +1.0 points on MBPP-Plus, which underscores the effectiveness of our framework. Our codes are available at https://github.com/ChiYeungLaw/AMR-Evol.

翻译：像GPT4这样的专有大语言模型在代码生成方面表现卓越，促使了通过知识蒸馏（例如Code Evol-Instruct）在开源模型中复现这些能力的趋势。然而，这些工作往往忽视了响应质量这一关键方面，严重依赖教师模型进行直接响应蒸馏。这种范式，特别是对于复杂指令，可能会降低合成数据的质量，从而损害知识蒸馏过程。为此，本研究提出了自适应模块化响应演化（AMR-Evol）框架，该框架采用两阶段流程来优化响应蒸馏。第一阶段，模块化分解，将直接响应分解为更易于管理的子模块。第二阶段，自适应响应演化，利用相关功能模块自动演化响应。我们在三个流行的代码基准测试（HumanEval、MBPP和EvalPlus）上的实验证明了AMR-Evol框架相对于基线响应蒸馏方法的优越性。通过与在相似数据规模上训练的开源代码大语言模型进行比较，我们观察到性能提升：在HumanEval-Plus上超过+3.0分，在MBPP-Plus上超过+1.0分，这凸显了我们框架的有效性。我们的代码可在https://github.com/ChiYeungLaw/AMR-Evol获取。