SCOPE: Language Models as One-Time Teachers for Hierarchical Planning

This research explores a novel application of language models in hierarchical planning, potentially improving efficiency in text-based environments. The use of a 'one-time teacher' approach could offer interesting implications for how AI agents are trained and utilized.

• •Investigates the use of language models in hierarchical planning within text environments.
• •Proposes a 'one-time teacher' strategy for training AI agents.
• •Potentially improves efficiency and effectiveness in text-based planning tasks.