Agent Tuning

Agent tuning focuses on optimizing the performance of large language models (LLMs) and other AI agents by fine-tuning them for specific tasks or environments, often using techniques like reinforcement learning and multi-agent systems. Current research emphasizes developing efficient and effective tuning methods, including self-improvement strategies where models learn from their own generated data, and exploring the use of diverse datasets, such as those incorporating negative examples or explicit class guidance, to improve model robustness and generalization. This work is significant because it addresses the limitations of current LLMs in real-world applications, enabling more adaptable and efficient AI agents for diverse domains, from personalized mobile assistants to complex robotic control.