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arXiv · 2506.02689

MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching

Liang Yue·Yihong Tang·Kehai Chen·Jie Liu·Min Zhang
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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.

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Liang Yue, Yihong Tang, Kehai Chen, Jie Liu, Min Zhang. 2025-06-03. MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching. https://arxiv.org/abs/2506.02689

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