A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models· 多智能体大模型系统自动调优神经网络
Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usually manual. This paper addresses the automatic tuning of these components and proposes a framework transferable across models of different nature. The multi-agent large language model (LLM) framework has three roles---analyst, coder, and recorder~---with an experiment log and a sandboxed execution environment. Unlike systems that optimize the entire ML script or search architectures in a discrete space, the framework operates over an open but semantically narrow space: the agent generates code only for the specified components, leaving the overall network structure unchanged. The approach is validated in two directions: tuning a classifica
多智能体大模型系统用于神经网络模型的自动调优。
- 核心方法
- 提出了一个多智能体系统，包含分析师、编码器和记录器三个角色，在不变的整体网络结构下自动生成和优化指定组件的代码。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 解决机器学习方法中关键组件的手动选择和调优问题。
- 关键实验
- 验证了在分类任务中的调优效果，但未详述具体实验数据。
- 主要贡献
- 提供了一种跨模型自动调优的方法框架，减少手动干预，提高调优效率。
- 意义与局限
- 有助于简化神经网络模型的调优过程，但可能在模型结构创新方面有限制。