Planning Ability

Research on planning ability in large language models (LLMs) focuses on evaluating and improving their capacity to generate and execute multi-step plans to achieve goals, particularly in complex or uncertain environments. Current efforts involve benchmarking LLMs and large reasoning models (LRMs) against various tasks, exploring techniques like instruction tuning and integrating LLMs with external verification systems to enhance plan quality and reliability. These investigations are crucial for understanding the limitations of current AI systems and for developing more robust and reliable planning agents with applications in robotics, automation, and other fields requiring complex decision-making.