把自然语言说明书编译成能跑的小函数
写代码的人都知道：有些功能用规则写不出来，但每次调用大模型又慢又贵。这篇论文提出「编译即训练」:让大模型当老师，先按你的自然语言描述生成一批示例，再用这些示例训练一个极小的本地模型——编译产物就像普通软件一样，能存、能版本管理、能组合，跑起来不再依赖任何远程服务。在专门挑难的测试集上，它比之前的快速编译器准确率高出一截(83.6% 对 0 精确匹配)，代价是编译要花一分钟而不是几秒。它不是你明天就能用的工具，但方向很实在：把「调用大模型」从每次都要联网的运行时操作，变成一次性的编译期成本。
📄 原文摘要(英文)
Many recurring text functions are easy to describe but difficult to implement with rules, while calling a large remote model for every input introduces repeated cost, latency, and dependency on a provider. We present compile by training, which turns a natural-language specification into a reusable neural function. At compile time, teacher models generate task-specific examples that are used to train a small adapter for a compact interpreter. The resulting function runs without the teachers and can be stored, versioned, and composed like ordinary software. On FuzzyBench-Hard, a subset on which the Program-as-Weights fast compiler produced no exact matches, compile by training reaches 83.6% semantic accuracy. This higher accuracy comes with a higher compile-time cost: roughly a minute rather than seconds for the fast compiler. We deploy the compiler in a public interactive service and demonstrate compiled functions in a multi-site website helper, a language-controlled 3D avatar, and a bidirectional English-Claudish translator.