AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback

• URL: http://arxiv.org/abs/2309.17176v3
• Date: Fri, 3 May 2024 08:24:12 GMT
• Title: AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback
• Authors: Wanpeng Zhang, Zongqing Lu,
• Abstract summary: Large Language Models (LLMs) have demonstrated significant success across various domains. Their application in complex decision-making tasks frequently necessitates intricate prompt engineering or fine-tuning. We introduce AdaRefiner, a novel framework designed to enhance the synergy between LLMs and RL feedback. 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.
• Score: 37.22370177877156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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