Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significant advancements in LLMs, their effective operation still relies heavily on human input to accurately guide the dialogue flow, with agent tuning being a crucial optimization technique that involves human adjustments to the model for better response to such guidance.Addressing this dependency, our work introduces the TinyAgent model, trained on a meticulously curated high-quality dataset. We also present the Collaborative Multi-Agent Tuning (CMAT) framework, an innovative system designed to augment language agent capabilities through adaptive weight updates based on environmental feedback. This framework fosters collaborative learning and real-time adaptation among multiple intelligent agents, enhancing their context-awareness and long-term memory. In this research, we propose a new communication agent framework that integrates multi-agent systems with environmental feedback mechanisms, offering a scalable method to explore cooperative behaviors. Notably, our TinyAgent-7B model exhibits performance on par with GPT-3.5, despite having fewer parameters, signifying a substantial improvement in the efficiency and effectiveness of LLMs.

翻译：开放大型语言模型（LLMs）显著推动了自然语言处理领域的发展，在各类任务中展现出卓越性能。尽管LLMs取得了重大进展，但其有效运行仍高度依赖人类输入以精确引导对话流程，其中智能体微调作为关键优化技术，涉及通过人类对模型的调整以更好地响应此类引导。为应对这一依赖性问题，本研究提出了TinyAgent模型，该模型基于精心筛选的高质量数据集进行训练。同时，我们引入了协作式多智能体微调（CMAT）框架——一种创新系统，通过基于环境反馈的自适应权重更新来增强语言智能体能力。该框架促进多个智能体间的协作学习与实时适应，提升其上下文感知能力与长期记忆。在本研究中，我们提出了一种融合多智能体系统与环境反馈机制的新型通信智能体框架，为探索协作行为提供了可扩展的方法。值得注意的是，我们提出的TinyAgent-7B模型尽管参数量更少，其性能与GPT-3.5相当，标志着LLMs效率与效果的显著提升。