Xue Yan, Yan Song, Xinyu Cui, Filippos Christianos, Haifeng Zhang, David Henry Mguni, Jun Wang

Large language models (LLMs) demonstrate their promise in tackling complicated practical challenges by combining action-based policies with chain of thought (CoT) reasoning. Having high-quality prompts on hand, however, is vital to the framework's effectiveness. Currently, these prompts are handcrafted utilizing extensive human labor, resulting in CoT policies that frequently fail to generalize. Human intervention is also required in order to develop grounding functions that ensure low-level controllers appropriately process CoT reasoning. In this paper, we take the first step towards a fully integrated end-to-end framework for task-solving in real settings employing complicated reasoning. To that purpose, we offer a new leader-follower bilevel framework capable of learning to ask relevant questions (prompts) and subsequently undertaking reasoning to guide the learning of actions to be performed in an environment. A good prompt should make introspective revisions based on historical findings, leading the CoT to consider the anticipated goals. A prompt-generator policy has its own aim in our system, allowing it to adapt to the action policy and automatically root the CoT process towards outputs that lead to decisive, high-performing actions. Meanwhile, the action policy is learning how to use the CoT outputs to take specific actions. Our empirical data reveal that our system outperforms leading methods in agent learning benchmarks such as Overcooked and FourRoom.