Synthesizing post-training data for LLMs through multi-agent simulation

Conference item

Post-training is essential for enabling large language models (LLMs) to follow human instructions. However, its effectiveness depends on high-quality instruction data, which is challenging to obtain in the real world due to privacy concerns, data scarcity, and high annotation costs. To fill this gap, inspired by the recent success of using LLMs to simulate human society, we propose MATRIX, a multi-agent simulator that automatically generates diverse text-based scenarios, capturing a wide range of real-world human needs in a realistic and scalable manner. Leveraging these outputs, we introduce a novel scenario-driven instruction generator MATRIX-Gen for controllable and highly realistic data synthesis. Extensive experiments demonstrate that our framework effectively generates both general and domain-specific data. On AlpacaEval 2 and Arena-Hard benchmarks, Llama-3-8B-Base, post-trained on datasets synthesized by MATRIX-Gen with just 20K instruction-response pairs, outperforms Meta’s Llama-3-8B-Instruct model, which was trained on over 10M pairs.

Authors: Tang, S; Pang, X; Liu, Z; Tang, B; Ye, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for Computational Linguistics
• Host title: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
• Pages: 23306-23335
• Publication date: 2025-07-01
• Acceptance date: 2025-05-15
• Event title: 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)
• Event location: Vienna, Austria
• Event website: https://2025.aclweb.org/
• Event start date: 2025-07-27
• Event end date: 2025-08-01
• DOI: 10.18653/v1/2025.acl-long.1136
• ISSN: 0736-587X
• EISBN: 9798891762510
• Language: English