Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning , serving open and powerful alternatives to commercial LLMs for agent tasks.

AgentTuning是一种简单且通用的方法，可以提高大型语言模型在代理任务方面的能力，同时保持其一般能力。该方法通过使用AgentInstruct与通用领域的开源指令相结合的混合指令调整策略对Llama 2系列进行了指令调整，从而得到AgentLM。评估结果显示，AgentTuning能够提升语言模型的代理能力而不影响其一般能力，AgentLM-70B在未知代理任务上与GPT-3.5-turbo相媲美，展现了广义的代理能力。我们在指定的网址开源了AgentInstruct和AgentLM-7B、13B和70B模型，为代理任务提供了开源和强大的替代方案。

AgentTuning：为LLMs赋予通用的代理能力