[R] Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models - University of Illinois 2023 - Achieves 94.4\% for programming on HumanEval with GPT-4 and 86.9\% with GPT-3.5 20\% better than with reflexion!

Paper: [https://arxiv.org/abs/2310.04406](https://arxiv.org/abs/2310.04406)

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 …

abstract acting agents autonomous autonomous agents capabilities decision deployment framework general inspiration language language models large language large language models llms machinelearning making performance planning processes reasoning search simple tasks tree