Compile by Training: Turning Natural-Language Specifications into Local Neural Functions

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 at compile time, a small adapter is trained for a compact interpreter, eliminating the need for remote model calls during execution. The approach achieves 83.6% semantic accuracy on FuzzyBench-Hard, though it incurs a higher compile-time cost of about a minute, and is demonstrated in applications such as a multi-site website helper, a language-controlled 3D avatar, and a bidirectional English‑Claudish translator.

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