Suboptimal training trajectories actively hurt long-horizon planning.

arXiv 2607.24720 builds a unified controlled multi-turn environment to isolate where planning ability comes from, across pre-training acquisition, post-training shaping via GRPO and on-policy distillation, and integration through multi-teacher on-policy distillation. Findings: pre-training data containing explicit world models improves generalization, suboptimal trajectories degrade performance specifically over long horizons, and cross-environment learning works when planning patterns are compatible but interferes when they conflict. For anyone curating agent training data, "fewer trajectories beats worse trajectories" is the actionable one. (arXiv 2607.24720)