A da R efiner: Refining Decisions of Language Models with Adaptive Feedback (2024)

Abstract: Large Language Models (LLMs) have demonstrated significant success across various domains. However, their application in complex decision-making tasks frequently necessitates intricate prompt engineering or fine-tuning, leading to challenges in unseen downstream tasks and heavy demands on computational resources. Meanwhile, Reinforcement Learning (RL) has been recognized as effective in decision-making problems but struggles in environments with sparse rewards, such as open-world games. To overcome these challenges, we introduce AdaRefiner, a novel framework designed to enhance the synergy between LLMs and RL feedback. The key component of AdaRefiner is a lightweight Adapter Language Model (LM), which automatically refines task comprehension based on feedback from RL agents. This method mitigates the need for intricate prompt engineering and intensive LLM fine-tuning while maintaining the LLMs’ generalization abilities and enhancing their decision-making capabilities in downstream tasks. Empirical evaluations of AdaRefiner on 22 diverse tasks within the open-world game Crafter have demonstrated its superior effectiveness, especially in guiding agents towards higher-level and common-sense skills. Our work makes contributions to the automatic self-refinement of LLMs with RL feedback, offering a more adaptable and efficient solution for complex decision-making problems. The code is available at https://github.com/PKU-RL/AdaRefiner.

## introduction the rapid development of large language models (llms), trained on massive corpora, has opened new frontiers in various fields, leveraging their ability to process and generate text @xcite . notably, llms have demonstrated impressive performance in decision-making problems (yao et al., ## experiment our experiments primarily aim to validate the following claims: 1) the integration of the adapter lm can enhance llm's comprehension of downstream tasks and the agent's understanding capability, resulting in more meaningful guidance; 2) agents trained under the adarefiner framework can exhibit superior performance and demonstrate higher-level decision-making capabilities.