新闻
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.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers studied how AI agents learn multi-step planning through pre-training, post-training refinement, and multi-teacher knowledge integration using controlled environments.
- 为何重要
- Engineers building foundation model agents need this when designing training pipelines for long-horizon planning tasks and understanding data quality tradeoffs.
- 注意
- Study uses controlled synthetic environments, not real-world data. Findings about trajectory quality and teacher compatibility may not transfer to production settings.
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