  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.
