Researchers propose Branching On-Policy Distillation, an improvement to on-policy distillation for AI agents. It selects informative decision points and applies teacher supervision to both main trajectories and branches, reducing student-generated token costs by 19-31%. The approach outperforms existing methods across various benchmarks.