Computer Science > Artificial Intelligence
[Submitted on 29 Sep 2023 (v1), last revised 3 May 2024 (this version, v3)]
Title:AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback
View PDF HTML (experimental)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.
Submission history
From: Wanpeng Zhang [view email][v1] Fri, 29 Sep 2023 12:16:19 UTC (3,119 KB)
[v2] Sat, 16 Dec 2023 03:45:19 UTC (2,284 KB)
[v3] Fri, 3 May 2024 08:24:12 UTC (2,286 KB)
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