Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
Primary Area: general machine learning (i.e., none of the above)
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Keywords: large language models, agent, reasoning, decision-making
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TL;DR: We propose a framework based on planning, reasoning and acting to improve decision-making for language agents
Abstract: While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents. We introduce LATS (Language Agent Tree Search), a general framework that synergizes the capabilities of LLMs in planning, acting, and reasoning. Drawing inspiration from Monte Carlo tree search in model-based reinforcement learning, LATS employs LLMs as agents, value functions, and optimizers, repurposing their latent strengths for enhanced decision-making. What is crucial in this method is the use of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that moves beyond the limitations of existing methods. Our experimental evaluation across diverse domains, such as programming, HotPotQA, and WebShop, demonstrates the superiority and versatility of LATS for both reasoning and acting. In particular, LATS achieves 94.4\% for programming on HumanEval with GPT-4 and an average score of 75.9 for web browsing on WebShop, demonstrating the effectiveness and generality of our method.
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Submission Number: 4113
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