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

arXiv:2609.04199 · cs.CL, cs.AI, cs.LG · Submitted 2026-09-03 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-09-03

Updated: 2026-09-03

Comments: EMNLP 2026 System Demonstrations. Demo: https://programasweights.com

Code: https://github.com/programasweights/paw-helper

License: http://creativecommons.org/licenses/by/4.0/

The gist: 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

Terminology

Abstract

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.

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