CodeEvo：通过混合和迭代反馈以交互驱动方式合成以代码为中心的数据
CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
AI总结：
为解决高质量指令-代码对获取难题，提出双智能体架构CodeEvo，审查器制定模式并结合混合验证协议，构建CodeEvo-100K数据集，实验表明基于此数据微调的模型在代码生成基准测试中表现出色。
摘要：
获取高质量的指令-代码对对于训练用于代码生成的大语言模型至关重要。虽然自动合成已成为昂贵手动策划的替代方案，但当前方法通常依赖严格的启发式方法，产生的数据缺乏依据或逻辑复杂性不足。我们提出了CodeEvo，一种双智能体架构，包括用于迭代解决方案合成的编码器和用于编排生成轨迹的审查器。审查器通过指令和代码的交错合成来制定模式，以系统地构建逻辑和复杂性，超越现有启发式方法的局限性。该过程通过将确定性编译器反馈与语义评估相结合的混合验证协议得到进一步加强。在此框架下，我们构建了CodeEvo-100K，一个具有逐步难度级别的指令-代码对的大规模数据集。广泛的实验表明，在CodeEvo数据上微调的模型在代码生成基准测试中始终优于既定的基线。深入分析进一步提供了对有效的以代码为中心的数据合成的见解。代码和数据可在该https URL上获取。
英文摘要：
Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation. While automated synthesis has emerged as an alternative to expensive manual curation, current approaches often rely on rigid heuristics, yielding data that is ungrounded or lacks logical complexity. We propose CodeEvo, a dual-agent architecture comprising a Coder for iterative solution synthesis and a Reviewer to orchestrate the generation trajectory. To transcend the limitations of existing heuristics, the Reviewer formulates a Schema to systematically architect logic and complexity through an interleaved synthesis of instructions and code. This process is further reinforced by a hybrid verification protocol synergizing deterministic compiler feedback with semantic evaluation. Under this framework, we construct CodeEvo-100K, a large-scale dataset of instruction-code pairs with stepped difficulty levels. Extensive experiments demonstrate that models fine-tuned on CodeEvo data consistently outperform established baselines across code generation benchmarks. In-depth analyses further provide insights into effective code-centric data synthesis. Code and data are available at https://github.com/QiushiSun/CodeEvo.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
