MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching
Liang Yue, Yihong Tang, Kehai Chen, Jie Liu, Min Zhang
Abstract
Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-following capabilities and task-specific performance. However, obtaining high-quality fine-tuning data for large models is challenging due to data collection difficulties and high production costs. To address this, we propose MASTER, a novel data augmentation method that enriches original data through interactions among multiple agents with varying cognitive levels. We simulate three pedagogically grounded teaching scenarios, leveraging multi-agent conversations to generate high-quality teacher-student interaction data. Utilizing MASTER, we construct BOOST-QA, a fine-tuning dataset augmented from existing datasets like Orca-Math-200k, ProcQA, and OpenHermes2.5. Experiments show that models fine-tuned with BOOST-QA perform excellently across multiple benchmarks, demonstrating strong multitask generalization. Notably, MASTER significantly improves models' reasoning abilities in complex tasks, providing valuable insights for future research.
BibTeX
@inproceedings{
yue2025master,
title={{MASTER}: Enhancing Large Language Model via Multi-Agent Simulated Teaching},
author={Liang Yue and Yihong Tang and Kehai Chen and Jie Liu and Min Zhang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=5GaDcRVgBw}
}