系统梳理代码大模型数据合成技术，助你高效训练更优代码生成模型。
Mastering the Craft of Data Synthesis for CodeLLMs
- 构建代码数据合成与筛选的技术分类体系
- 总结近期进展并指出关键挑战与改进方向
- 为新研究者提供实用入门指南
大型语言模型（LLMs）在代码理解与生成方面表现卓越，使编程任务成为研究热点，兼具实际应用价值和作为大模型评估基准的意义。数据合成与过滤技术在此领域被广泛采用，且已被证明极为有效。本文系统地回顾并构建了这些技术的分类体系，重点聚焦最新进展。我们揭示了关键技术挑战，探讨了未来研究方向，并为初入该领域的研究者提供了实用指导。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data synthesis and filtering techniques have been widely adopted and shown to be highly effective in this context. In this paper, we present a focused survey and taxonomy of these techniques, emphasizing recent advancements. We highlight key challenges, explore future research directions, and offer practical guidance for new researchers entering the field.
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