Enhancing Decision-Making of Large Language Models via Actor-Critic

Heng Dong; Kefei Duan; Chongjie Zhang

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arXiv · 2506.06376

Enhancing Decision-Making of Large Language Models via Actor-Critic

Heng Dong·Kefei Duan·Chongjie Zhang

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Abstract

Large Language Models (LLMs) have achieved remarkable advancements in natural language processing tasks, yet they encounter challenges in complex decision-making scenarios that require long-term reasoning and alignment with high-level objectives. Existing methods either rely on short-term auto-regressive action generation or face limitations in accurately simulating rollouts and assessing outcomes, leading to sub-optimal decisions. This paper introduces a novel LLM-based Actor-Critic framework, termed LAC, that effectively improves LLM policies with long-term action evaluations in a principled and scalable way. Our approach addresses two key challenges: (1) extracting robust action evaluations by computing Q-values via token logits associated with positive/negative outcomes, enhanced by future trajectory rollouts and reasoning; and (2) enabling efficient policy improvement through a gradient-free mechanism. Experiments across diverse environments -- including high-level decision-making (ALFWorld), low-level action spaces (BabyAI-Text), and large action spaces (WebShop) -- demonstrate the framework's generality and superiority over state-of-the-art methods. Notably, our approach achieves competitive performance using 7B/8B parameter LLMs, even outperforming baseline methods employing GPT-4 in complex tasks. These results underscore the potential of integrating structured policy optimization with LLMs' intrinsic knowledge to advance decision-making capabilities in multi-step environments.

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BibTeX RIS

Heng Dong, Kefei Duan, Chongjie Zhang. 2025-06-04. Enhancing Decision-Making of Large Language Models via Actor-Critic. https://arxiv.org/abs/2506.06376

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