OpenAI新方法把自然语言描述变成可复用的本地神经函数，比远程调用更快，还部署了实际应用案例。
研究人员提出'通过训练编译'方法，将自然语言规范转化为可重用的神经函数。该方法使用教师模型生成任务特定示例，训练小型适配器供紧凑解释器使用。在FuzzyBench-Hard基准测试上，该方法达到83.6%语义准确率，编译时间约为一分钟。编译后的函数已应用于网站助手、3D头像和英-克劳迪什双向翻译器。
Compile by Training: Turning Natural-Language Specifications into Local Neural Functions
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.