Training small models to copy agent workflows may cut AI costs

Token-based billing is pushing companies to look again at small language models. The research describes training a small language model on traces from agent workflows run by frontier models. After supervised fine-tuning, the small model may reach close to frontier-level results while costing far less.

The main idea is to avoid calling expensive models through many agent steps every time, and instead teach part of that process to a cheaper model. Real-world results are still an open question.