TL;DR A new method runs a large teacher LLM just once to "compile by training" a small, dedicated neural function straight from a natural-language spec. It pushes accuracy on a hard benchmark way up in just minutes. Title: Compile by Training: Turning Natural-Language Specifications into Local Neural Functions URL: 🎯 A teacher mix of GPT-5.4-mini and GPT-5.5 (2:1) auto-generates input-output examples, which are baked into Qwen3-0.6B on the spot via LoRA (rank 64) ⚡ On the hard FuzzyBench-Hard benchmark, LEM accuracy jumps from 0.224 for the fast compiler to 0.836 🕒 Compiling takes about 51 seconds on a B300 GPU, but running teacher data synthesis and training in parallel keeps the GPU from sitting idle 🌐 Validated across real use cases: a QA assistant spanning 4 sites (28 of 30 compiled programs in production) and a natural-language-to-3D-DSL controller (43 of 44 commands succeeded) 📈 Ablations on teacher mixing and data scale show accuracy saturating around 2,400-3,600 unique pairs 💬 Instead of calling a giant model every single time, training it once and carrying home a small function seems like a great fit for medium-complexity tasks that run often #LLM# #LoRA#