tinyR1-32B-preview-exl2

Model Name: Tiny-R1-32B-Preview
Title: SuperDistillation Achieves Near-R1 Performance with Just 5% of Parameters.

Introduction

We introduce our first-generation reasoning model, Tiny-R1-32B-Preview, which outperforms the 70B model Deepseek-R1-Distill-Llama-70B and nearly matches the full R1 model in math.

Evaluation

All scores are reported as pass@1. For AIME 2024, we sample 16 responses, and for GPQA-Diamond, we sample 4 responses, both using average overall accuracy for stable evaluation.

Approach

We applied supervised fine-tuning (SFT) to Deepseek-R1-Distill-Qwen-32B across three target domains—Mathematics, Code, and Science — using the 360-LLaMA-Factory training framework to produce three domain-specific models. We used questions from open-source data as seeds, and used DeepSeek-R1 to generate responses for mathematics, coding, and science tasks separately, creating specialized models for each domain. Building on this, we leveraged the Mergekit tool from the Arcee team to combine multiple models, creating Tiny-R1-32B-Preview, which demonstrates strong overall performance.

Data

1. Math

58.3k CoT trajectories from open-r1/OpenR1-Math-220k, default subset

2. Coding

19k CoT trajectories open-thoughts/OpenThoughts-114k, coding subset

3. Science

We used R1 to generate 8 CoT trajectories on 7.6k seed examples, and got 60.8k CoT trajectories in total; the seed examples are as follows:

• 2.7k seed examples from simplescaling/data_ablation_full59K, science and health science subset
• 4.9k seed examples from open-thoughts/OpenThoughts-114k, science subset

Open Source Plan

We will publish a technical report as soon as possible and open-source our training and evaluation code, selected training data, and evaluation logs. Having benefited immensely from the open-source community, we are committed to giving back in every way we can.

Contributors

360 Team: Lin Sun, Guangxiang Zhao, Xiaoqi Jian, Weihong Lin, Yongfu Zhu, Change Jia, Linglin Zhang, Jinzhu Wu, Sai-er Hu, Xiangzheng Zhang

PKU Team: Yuhan Wu, Zihan Jiang, Wenrui Liu, Junting Zhou, Bin Cui, Tong Yang

Citation

@misc{tinyr1proj,
      title={SuperDistillation Achieves Near-R1 Performance with Just 5% of Parameters.}, 
      author={TinyR1 Team},
      year={2025},
      eprint={},
      archivePrefix={},
      primaryClass={},
      url={https://huggingface.co/qihoo360/TinyR1-32B-Preview}, 
}