Keegan Wang and colleagues showed how to build a cheap scheduling assistant for home‑care agencies. They first collected real‑time yes/no safety decisions from the large GPT‑5.4‑nano model. Then they fine‑tuned a small 8‑billion‑parameter Qwen3 model on 11.6 k of those decisions. The small model agrees with the teacher 91.3% of the time, runs about 2.5 times faster, and costs a fraction of the original.
Why it matters
Home‑care agencies can deploy a low‑cost, faster model without sacrificing decision quality.
