The short version
Distillation works when the task is narrow, the volume is high and you have logged traces. Train on rationales rather than labels alone, benchmark against the teacher on a frozen set, and keep the frontier model as the escalation path.
Distillation is not new, but the enterprise version of it has a specific shape: you already pay a frontier model to do one repetitive job thousands of times a day, and every one of those calls is a labelled training example you are throwing away.
Train on reasoning, not just answers
The 'Distilling step-by-step' line of work showed that training smaller models on teacher-generated rationales, rather than labels alone, produces stronger students with less data — in the reported experiments, small models trained this way outperformed much larger baselines on the target task. Multi-teacher variants push the same idea further by transferring reasoning from several sources.
Practically, that means your capture pipeline should log the teacher's reasoning trace and intermediate structure — extracted fields, chosen category, cited passage — not only the final string. Retro-fitting that logging later costs you months of otherwise free training data.
The pipeline we run
- 01Scope one task with a stable output contract: JSON fields, a fixed label set, or a bounded summary form.
- 02Freeze an evaluation set of 300 to 1,000 real cases, labelled by a domain expert, with the hard cases deliberately over-represented.
- 03Capture teacher outputs plus rationales at volume; de-duplicate and filter obvious teacher errors.
- 04Fine-tune a small open base model — supervised on the traces, then preference or rejection sampling on the cases it gets wrong.
- 05Benchmark student against teacher on the frozen set: task accuracy, latency, cost per thousand calls, and failure modes side by side.
- 06Deploy with a confidence threshold that escalates uncertain cases to the frontier model, and keep logging so the next generation trains on the escalations.
When it pays and when it does not
| Signal | Distil | Stay on the frontier model |
|---|---|---|
| Task breadth | One narrow job | Open-ended, unpredictable requests |
| Volume | High and steady | Low or spiky |
| Traces available | Months of logs | Cold start, no data |
| Latency need | Tight or on-device | Seconds are fine |
| Residency need | Region-locked or offline | Cloud API acceptable |
| Ops appetite | Willing to run inference | No platform team |
The honest trade is operational. A small model you host means capacity planning, GPU or edge hardware, quantisation choices and a retraining cadence. In exchange you get predictable unit cost, a model that runs in your region or on a device, and weights you own.
Check your teacher model's terms before training on its outputs. Some providers restrict using outputs to train competing models; the safe pattern is a clear task scope, documented consent for your own data, and legal sign-off before the run.
What we would do in a pilot
A benchmarked comparison on your own frozen evaluation set: distilled student versus current frontier calls, with accuracy, latency and cost per thousand calls reported per case class — plus the training and eval pipeline handed over.
Sources
- 01Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data — arXiv
- 02Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation — arXiv
- 03API pricing (for teacher-cost baselines) — OpenAI
Figures drawn from our own engagements elsewhere on this site are illustrative placeholders. Anything attributed above links to its public source.