While Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks, automating complex, real-world workflows, such as LLM training, remains a significant challenge. In this paper, we introduce TREX, a multi-agent system that automates the entire LLM training life-cycle. By orchestrating collaboration between two core modules-the Researcher and the Executor-the system seamlessly performs requirement analysis, open-domain literature and data research, formulation of training strategies, preparation of data recipes, and model training and evaluation. The multi-round experimental process is modeled as a search tree, enabling the system to efficiently plan exploration paths, reuse historical results, and distill high-level insights from iterative trials. To evaluate the capability of automated LLM training, we construct FT-Bench, a benchmark comprising 10 tasks derived from real-world scenarios, ranging from optimizing fundamental model capabilities to enhancing performance on domain-specific tasks. Experimental results demonstrate that the TREX agent consistently optimizes model performance on target tasks.

翻译：尽管大语言模型（LLMs）已赋能AI研究智能体执行独立的科学任务，但自动化诸如LLM训练这类复杂的真实世界工作流仍是一项重大挑战。本文提出TREX——一个自动化完整LLM训练生命周期的多智能体系统。通过协调"研究员"与"执行者"两个核心模块的协作，该系统能够无缝完成需求分析、开放域文献与数据研究、训练策略制定、数据方案准备以及模型训练与评估。我们将多轮实验过程建模为搜索树，使系统能够高效规划探索路径、复用历史结果，并从迭代试验中提炼高层洞见。为评估自动化LLM训练的能力，我们构建了FT-Bench基准测试集，包含10个源自真实场景的任务，涵盖从基础模型能力优化到领域特定任务性能增强。实验结果表明，TREX智能体能够持续优化目标任务的模型性能。