LLMPC: Large Language Model Predictive Control

Recent advancements in planning prompting techniques for Large Language Models have improved their reasoning, planning, and action abilities. This paper develops a planning framework for Large Language Models using model predictive control that enables them to iteratively solve complex problems with long horizons. We show that in the model predictive control formulation, LLM planners act as approximate cost function optimizers and solve complex problems by breaking them down into smaller iterative steps. With our proposed planning framework, we demonstrate improved performance over few-shot prompting and improved efficiency over Monte Carlo Tree Search on several planning benchmarks.