Can111/m1-32b

Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

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. M1-32B is optimized for improved reasoning, discussion, and decision-making in multi-agent systems (MAS), including frameworks such as AgentVerse.

Code: https://github.com/jincan333/MAS-TTS

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🚀 Key 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.

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🏗️ 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

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📊 Performance

Model	General Understanding		Mathematical Reasoning		Coding
	GPQA	Commongen	AIME2024	MATH-500	HumanEval	MBPP-S
Non-Reasoning Models
Qwen2.5	50.2	96.7	21.1	84.4	89.0	80.2
DeepSeek-V3	58.6	98.6	33.3	88.6	89.6	83.9
GPT-4o	49.2	97.8	7.8	81.3	90.9	85.4
Reasoning Models
s1.1-32B	58.3	94.1	53.3	90.6	82.3	77.4
DeepSeek-R1	75.5	97.2	78.9	96.2	98.2	91.7
o3-mini	71.3	99.1	84.4	95.3	97.0	93.6
M1-32B (Ours)	61.1	96.9	60.0	95.1	92.8	89.1
M1-32B w. CEO (Ours)	62.1	97.4	62.2	95.8	93.9	90.5

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.

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💬 Intended Use

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

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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}
}