Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining
Authors: name: Tianyi Bai, name: Ling Yang, name: Zhen Hao Wong, name: Jiahui Peng, name: Xinlin Zhuang, name: Chi Zhang, name: Lijun Wu, name: Qiu Jiantao, name: Wentao Zhang, name: Binhang Yuan, name: Conghui He

Efficient data selection is crucial to accelerate the pretraining of large
language models (LLMs). While various methods have been proposed to enhance
data efficiency, limited research has addressed the inherent conflicts between
these approaches to achieve optimal data selection for LLM pretraining. To
tackle this problem, we propose a novel multi-agent collaborative data
selection mechanism. In this framework, each data selection method serves as an
independent agent, and an agent console is designed to dynamically integrate
the information from all agents throughout the LLM training process. We conduct
extensive empirical studies to evaluate our multi-agent framework. The
experimental results demonstrate that our approach significantly improves data
efficiency, accelerates convergence in LLM training, and achieves an average
performance gain of 10.5% across multiple language model benchmarks compared to
the state-of-the-art methods.