Training AI models has always been challenging, especially when there is a need for custom models to provide personalized services. Algorithm engineers often face a lengthy process to iteratively develop models tailored to specific business requirements, making it even more difficult for non-experts. The quest for high-quality and efficient model development, along with the emergence of Large Language Model (LLM) Agents, has become a key focus in the industry. Leveraging the powerful analytical, planning, and decision-making capabilities of LLM, we propose a TrainerAgent system comprising a multi-agent framework including Task, Data, Model and Server agents. These agents analyze user-defined tasks, input data, and requirements (e.g., accuracy, speed), optimizing them comprehensively from both data and model perspectives to obtain satisfactory models, and finally deploy these models as online service. Experimental evaluations on classical discriminative and generative tasks in computer vision and natural language processing domains demonstrate that our system consistently produces models that meet the desired criteria. Furthermore, the system exhibits the ability to critically identify and reject unattainable tasks, such as fantastical scenarios or unethical requests, ensuring robustness and safety. This research presents a significant advancement in achieving desired models with increased efficiency and quality as compared to traditional model development, facilitated by the integration of LLM-powered analysis, decision-making, and execution capabilities, as well as the collaboration among four agents. We anticipate that our work will contribute to the advancement of research on TrainerAgent in both academic and industry communities, potentially establishing it as a new paradigm for model development in the field of AI.

翻译：摘要：人工智能模型的训练始终充满挑战，尤其在需要定制化模型以提供个性化服务时更为突出。算法工程师往往需要经历漫长的迭代开发过程才能满足特定业务需求，而非专业人士则更难以参与其中。在追求高质量与高效率模型开发的背景下，大语言模型智能体的出现已成为行业关注焦点。我们利用大语言模型强大的分析、规划与决策能力，提出TrainerAgent系统，该系统包含任务智能体、数据智能体、模型智能体和服务智能体组成的多智能体框架。这些智能体通过分析用户定义的任务、输入数据及需求（如准确率、速度），从数据与模型两个维度进行综合优化，最终获得满意的模型并将其部署为在线服务。在计算机视觉与自然语言处理领域的经典判别式与生成式任务上的实验评估表明，本系统始终能产出满足预期标准的模型。此外，系统展现出关键识别与拒绝不可完成任务（如幻想场景或不道德请求）的能力，确保了鲁棒性与安全性。本研究通过整合大语言模型驱动的分析、决策与执行能力，以及四个智能体间的协作，相较于传统模型开发方式，在实现更高效率与更优质量的目标模型方面取得了显著进展。我们预期该项工作将推动TrainerAgent在学术界与工业界的研究进展，并可能成为人工智能领域模型开发的新范式。