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While large language models (LLMs) have demonstrated impressive performanceon a range of decision-making tasks, they rely on simple acting processes andfall short of broad deployment as autonomous agents. We introduce LATS(Language Agent Tree Search), a general framework that synergizes thecapabilities of LLMs in planning, acting, and reasoning. Drawing inspirationfrom Monte Carlo tree search in model-based reinforcement learning, LATSemploys LLMs as agents, value functions, and optimizers, repurposing theirlatent strengths for enhanced decision-making. What is crucial in this methodis the use of an environment for external feedback, which offers a moredeliberate and adaptive problem-solving mechanism that moves beyond thelimitations of existing techniques. Our experimental evaluation across diversedomains, such as programming, HotPotQA, and WebShop, illustrates theapplicability of LATS for both reasoning and acting. In particular, LATSachieves 94.4% for programming on HumanEval with GPT-4 and an average score of75.9 for web browsing on WebShop with GPT-3.5, demonstrating the effectivenessand generality of our method.

Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models