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The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation

Research LLM Agents

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TL;DR - This paper studies how foundation-model agents acquire, refine, and combine long-horizon planning abilities in a controlled multi-turn environment. It identifies training-data and distillation strategies that improve generalization while exposing sources of error and capability interference.

  • Explicit chain-of-thought state-transition modeling improves long-horizon generalization; atomic skills alone do not enable composition, but limited long-horizon data can.
  • Suboptimal training trajectories substantially degrade performance as errors compound over longer horizons.
  • On-policy distillation (OPD) is effective across more low-quality and long-horizon settings than GRPO, owing to more consistent update directions.
  • Multi-teacher OPD can integrate compatible or partially shared planning patterns, while conflicting patterns cause severe interference.

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The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation

arXiv cs.CL Tianyi Men, Zhuoran Jin, Kang Liu, Jun Zhao 2026-07-27 arXiv:2607.24720
Public signals Hugging Face upvotes 26
Providers: Hugging Face · Upvotes 26 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-26 14:44:07.995167 UTC

TL;DR - This paper studies how foundation-model agents acquire, refine, and combine long-horizon planning abilities in a controlled multi-turn environment. It identifies training-data and distillation strategies that improve generalization while exposing sources of error and capability interference.

  • Explicit chain-of-thought state-transition modeling improves long-horizon generalization; atomic skills alone do not enable composition, but limited long-horizon data can.
  • Suboptimal training trajectories substantially degrade performance as errors compound over longer horizons.
  • On-policy distillation (OPD) is effective across more low-quality and long-horizon settings than GRPO, owing to more consistent update directions.
  • Multi-teacher OPD can integrate compatible or partially shared planning patterns, while conflicting patterns cause severe interference.
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