SynLogic
Collection
Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
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SynLogic-32B is a state-of-the-art reasoning model built on Qwen2.5-32B-Base and trained using reinforcement learning on our comprehensive SynLogic dataset. The model excels at logical reasoning tasks and demonstrates strong generalization to mathematical domains.
Model | BBEH | KOR-Bench | BBH |
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Qwen2.5-32B-Instruct | 17.5 | 54.7 | 84.5 |
DeepSeek-R1-Distill-Qwen-32B | 19.2 | 66.6 | 88.3 |
SynLogic-32B | 25.5 | 62.2 | 85.8 |
Key Achievement: +6 points improvement over DeepSeek-R1-Distill-Qwen-32B on the challenging BBEH benchmark, establishing state-of-the-art performance among open-source logical reasoning models.
@misc{liu2025synlogic,
title={SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond},
author={Junteng Liu and Yuanxiang Fan and Zhuo Jiang and Han Ding and Yongyi Hu and Chi Zhang and Yiqi Shi and Shitong Weng and Aili Chen and Shiqi Chen and Yunan Huang and Mozhi Zhang and Pengyu Zhao and Junjie Yan and Junxian He},
year={2025},
eprint={2505.19641},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.19641},
}