You can transfer capabilities from strong to weak models at inference time through clever prompt engineering and code scaffolding, achieving similar gains to training-time distillation without any parameter updates.
This paper shows that stronger AI models can help weaker ones solve tasks better at test time by building 'harnesses'—structured inference-time scaffolds like code templates and routing logic—without retraining. Testing on reasoning benchmarks, the approach nearly doubles weaker model performance by offloading unstable reasoning into deterministic code and enforcing strict output formats.