# Compile by Training：将自然语言规格编译为可复用的本地神经函数

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-03 08:00
- AIHOT 分数：46
- AIHOT 链接：https://aihot.virxact.com/items/cmtmkqql103uerosevx2avcaq
- 原文链接：https://arxiv.org/abs/2609.04199

## AI 摘要

论文提出 compile by training，把自然语言规格编译为可复用的神经函数：编译时由教师模型生成任务样例，训练紧凑解释器的小型 adapter，运行时无需教师模型，可像普通软件一样存储、版本化和组合。

## 正文

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.