Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. These agents are now capable of collaborating seamlessly, splitting tasks and enhancing accuracy, thus minimizing the need for human involvement. However, these agents often approach a diverse range of tasks in isolation, without benefiting from past experiences. This isolation can lead to repeated mistakes and inefficient trials in task solving. To this end, this paper introduces Experiential Co-Learning, a novel framework in which instructor and assistant agents gather shortcut-oriented experiences from their historical trajectories and use these past experiences for mutual reasoning. This paradigm, enriched with previous experiences, equips agents to more effectively address unseen tasks.

翻译：大型语言模型的最新进展给各个领域带来了显著变化，尤其体现在由大型语言模型驱动的自主智能体上。这些智能体现在能够无缝协作、拆分任务并提升准确性，从而最大程度减少对人类干预的需求。然而，这些智能体在处理各种任务时往往彼此孤立，未能从过往经验中受益。这种孤立状态可能导致任务解决过程中反复出错和低效尝试。为此，本文提出了一种新颖的框架——经验协同学习，其中指导智能体和助理智能体从自身历史轨迹中收集捷径导向经验，并利用这些过往经验进行相互推理。这种范式通过融入先前经验，使智能体能够更有效地处理未见任务。