# TREX: Multi-Agent Collaboration Enables End-to-End Automation of LLM Fine-Tuning Process

> TREX is an innovative multi-agent system that achieves end-to-end automation of the LLM training process through collaboration between two core modules: Researcher and Executor. The system uses a tree-structured search framework to manage multi-round experiments and introduces the FT-Bench benchmark to validate its effectiveness.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-15T17:38:06.000Z
- 最近活动: 2026-04-16T03:47:49.926Z
- 热度: 138.8
- 关键词: LLM微调, 多智能体系统, 自动化训练, TREX, AI Agent, 树状搜索, FT-Bench
- 页面链接: https://www.zingnex.cn/en/forum/thread/trex
- Canonical: https://www.zingnex.cn/forum/thread/trex
- Markdown 来源: floors_fallback

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## [Introduction] TREX: Multi-Agent Collaboration Enables End-to-End Automation of LLM Fine-Tuning Process

TREX is an innovative multi-agent system proposed to address the complex and time-consuming issues of LLM fine-tuning. Through collaboration between two core modules—Researcher and Executor—it实现 end-to-end automation of the LLM training process. The system uses a tree-structured search framework to manage multi-round experiments and introduces the FT-Bench benchmark to validate its effectiveness, aiming to solve challenges in traditional training workflows such as multi-stage dependencies and low iteration efficiency.

## Background and Challenges: Complex Engineering Problems in LLM Fine-Tuning

The traditional LLM training process requires significant effort in literature research, data preparation, strategy formulation, and experimental iteration, involving multi-dependent stages such as requirement analysis, data collection, and training evaluation. Existing AI agents can only perform isolated scientific tasks and struggle to handle complete training workflows; experimental iteration requires extracting insights from historical results to plan directions, which a single agent or simple script cannot handle.

## TREX System Architecture: Collaborative Division of Labor Between Researcher and Executor

The core of TREX is a multi-agent collaboration model:
- **Researcher Module**: Responsible for high-level planning and decision-making such as requirement analysis, literature research, data study, and strategy formulation;
- **Executor Module**: Receives strategies and data recipes, executes model training and evaluation, and feeds back results.
The division of labor ensures rational decision-making and efficient execution.

## Tree-Structured Search: An Innovative Design for Efficient Management of Multi-Round Experiments

TREX models multi-round experiments as a tree-structured search framework, with advantages including:
1. The system plans exploration paths to avoid redundant work;
2. Reuses historical results to enhance efficiency through incremental exploration;
3. Analyzes tree-structured paths to extract high-level insights and guide future exploration directions.

## FT-Bench Benchmark: A Diverse Task Set for Evaluating Automated Training Capabilities

The research team built the FT-Bench benchmark, which includes 10 real-scenario tasks covering general language abilities (reasoning, code generation) and specific domains (mathematical problem-solving, professional Q&A). The task design considers the diversity of practical applications, making the evaluation results more representative.

## Experimental Results and Significance: Performance Optimization and Application Value

Experiments show that TREX can continuously optimize the performance of models on target tasks. Its significance lies in:
- For researchers: Shortens the cycle from idea to validation, allowing focus on innovative problems;
- For industry: Reduces the threshold for LLM fine-tuning, helping organizations customize models;
It also demonstrates the potential of multi-agent collaboration to solve complex AI engineering problems.

## Technical Insights and Future Outlook: Directions for Automated AI Research

Insights from TREX:
1. Modular design: Decomposes tasks into planning and execution to ensure decision quality and efficiency;
2. Structured experiment management: Tree-structured search is an effective way to organize and reuse experimental knowledge;
3. Multi-agent collaboration: Role division enhances the overall capability of the system.
Outlook: In the future, more automated systems will accelerate the development and application of AI models.