LLM Planning

Large language model planning asks whether models can turn goals and instructions into ordered actions, then adapt those actions as constraints or environments change. Common approaches include chain-of-thought and tree search, hierarchical task decomposition, reinforcement learning, and hybrid systems that connect language models to PDDL, symbolic solvers, tools, or learned world models. Benchmarks such as BlocksWorld, TravelPlanner, and ALFWorld test plan validity, efficiency, and adaptation in settings from fixed puzzles to interactive environments. Models still struggle with long horizons, coupled constraints, and disruptions; verification and solver-based methods improve reliability but raise questions about cost, generalization, and how much planning models can do on their own.