The paper introduces "compile by training," a method that converts natural‑language specifications into reusable neural functions by generating task‑specific examples with teacher models and training a small adapter for a compact interpreter. This enables execution without relying on remote model calls, reducing cost, latency, and provider dependency. The approach produces functions that run locally after compile time.