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。

Generative AI models don’t process text the same way humans do. Understanding their “token”-based internal environments may help explain some of their strange behaviors — and stubborn limitations.

The process of turning sensitive data into a token or distinctive identifier while maintaining its value and link to the original data is known as data tokenization. This token stands in for the ...