When fine-tuning smaller models with expert demonstrations, don't copy entire expert trajectories—instead use on-policy correction to fix only failing steps in the model's own rollouts.
This paper shows how to improve smaller AI models on enterprise tasks by co-evolving two things: the system prompt and tool setup (harness) around the model, and the model's weights through fine-tuning. The key finding is that naive imitation learning fails because smaller models copy expert strategies they can't execute.