TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration

• URL: http://arxiv.org/abs/2604.14116v2
• Date: Wed, 22 Apr 2026 07:33:13 GMT
• Title: TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration
• Abstract summary: Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks.<n>In this paper, we introduce TREX, a multi-agent system that automates the entire LLM training life-cycle.
• Score: 31.598039827972727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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