M1 32b

🚀 M1-32B: A Large Language Model for Multi-Agent Systems

M1-32B is a 32B-parameter large language model fine-tuned from Qwen2.5-32B-Instruct on the M500 dataset—an interdisciplinary multi-agent collaborative reasoning dataset. It is optimized for improved reasoning, discussion, and decision-making in multi-agent systems (MAS), including frameworks such as AgentVerse.

🚀 Quick Start

For more information about using M1-32B, you can refer to the code repository: https://github.com/jincan333/MAS-TTS

✨ Features

• 🧠 Enhanced Collaborative Reasoning
  Trained on real multi-agent traces involving diverse roles like Expert Recruiter, Problem Solvers, and Evaluator.

• 🗣️ Role-Aware Dialogue Generation
  Learns to reason and respond from different expert perspectives based on structured prompts.

• ⚙️ Optimized for Multi-Agent Systems
  Performs well as a MAS agent with adaptive collaboration and token budgeting.

📦 Installation

This model is based on the transformers library. You can install the necessary dependencies according to the requirements of the transformers library.

📚 Documentation

🏗️ Model Training

• Base Model: Qwen2.5-32B-Instruct
• Dataset: M500 (500 curated multi-agent reasoning traces)
• Objective: Supervised Fine-Tuning (SFT) on role-conditioned prompts
• Training Setup:
  • 8 × A100 GPUs
  • 5 epochs
  • Learning rate: 1e-5
  • Frameworks: DeepSpeed, FlashAttention, LLaMA-Factory

📊 Performance

Table Caption:
Performance comparison on general understanding, mathematical reasoning, and coding tasks using strong reasoning and non-reasoning models within the AgentVerse framework. Our method achieves substantial improvements over Qwen2.5 and s1.1-32B on all tasks, and attains performance comparable to o3-mini and DeepSeek-R1 on MATH-500 and MBPP-S, demonstrating its effectiveness in enhancing collaborative reasoning in MAS. Note that the results of s1.1-32B are obtained without using budget forcing.

💬 Intended Use

M1-32B is intended for research on Multi-agent reasoning and collaboration in MAS

📄 License

This project is licensed under the Apache-2.0 license.

📖 Citation

If you use this model, please cite the relevant papers:

@article{jin2025two,
  title={Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning},
  author={Jin, Can and Peng, Hongwu and Zhang, Qixin and Tang, Yujin and Metaxas, Dimitris N and Che, Tong},
  journal={arXiv preprint arXiv:2504.09772},
  year={2025}
}