The paper introduces a unified, controlled multi‑turn environment that enables systematic study of long‑horizon planning in foundation model agents across distinct training stages. By replacing opaque internet data with a precise, controllable setting, the authors aim to clarify how planning ability is acquired, shaped, and integrated. This framework supports analysis from pre‑training through post‑training via single‑ and multi‑teacher on‑policy agentic distillation.