在代码大模型（Code LLMs）的预训练中，行业内长期存在一种惯性思维，即把所有编程语言的代码都视为同质化的文本数据，主要关注数据总量的堆叠。然而，现代软件开发本质上是多语言混合的，不同语言的语法特性、语料规模和应用场景差异巨大。如果忽略这些差异，笼统地应用通用的 Scaling Laws，往往会导致性能预测偏差和算力浪费。

这项由北京航空航天大学的杨健、国鑫、林静等研究者联合优矿公司和中国人民大学人工智能学院团队完成的突破性研究，发表于2025年12月的arXiv预印本（论文编号：2512.13472v1），是全球首次系统性探索多语言编程训练规律的重要成果。

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Katelyn Peters has a writer and editor for more than five years who focuses on both investing and personal finance content. In addition to her experience in finance, she is also a volunteer editorial ...