Researchers introduce Interactive-Policy Distillation (IPD), a method that applies adaptive teacher intervention to the student rollout in on-policy distillation, improving performance and data efficiency. IPD trains a student model on its self-generated trajectories with dense token-level teacher feedback, and demonstrates higher accuracy and efficiency compared to traditional on-policy distillation (OPD).