Large Language Models (LLMs) have the capacity to store and recall facts.

Exploration in sparse-reward reinforcement learning (RL) is difficult due to the need for long, coordinated sequences of actions in order to achieve any reward.

"Rule-of-Thumb" generation, show that our method consistently exceeds previous

This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer.