We recently ran similar experiments and saw that fine-tuning small models on automatically curated high-quality outputs from a large model can beat large-model performance while reducing inference costs by up to 30x and inference time by up to 4x.

We benchmarked closed-source (OpenAI, Google) and open-source (Qwen) models on multi-turn maze navigation (BabyAI), agentic RAG (Multi-Hop), and agentic tool use (τ-bench).

We're still running a few experiments and plan to update the post with additional results in a few days.

Looking forward to trying out importance weighting soon!

Curated Behavior Cloning: Small LLMs Can Beat Large Ones at 5-30x Lower Cost: https://www.tensorzero.com/blog/curated-behavior-cloning-sma...