Iterate on a frontier model, serve on an 8B: distilled scheduler agrees with production records 0.913 of the time, beating its own teacher
A home-care scheduling agent no longer asks a small model to handle a narrow task cold. Zero-shot small models failed it, and retraining on every behavior change was too slow, so the behavior was first iterated in prompt space on GPT-5.4-nano, then 11.6k recorded production decisions were LoRA-tuned into Qwen3-8B. That model agrees with recorded decisions 0.913 of the time, above the teacher's 72.7% self-agreement when re-run on its own prompts.