---
title: SynLogic-32B
canonical_url: "https://www.modelscope.cn/models/MiniMax/SynLogic-32B"
md_url: "https://www.modelscope.cn/models/MiniMax/SynLogic-32B.md"
repository: MiniMax/SynLogic-32B
last_updated: 2025-06-10
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - Qwen/Qwen2.5-32B
base_model_relation: finetune
parameters: 32.8B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 561
stars: 2
tags:
  - LLM
---

# SynLogic-32B

> SynLogic-32B - MiniMax 在 ModelScope 开源的模型。SynLogic-32B: Advanced Logical Reasoning Model

MiniMax/SynLogic-32B 是 ModelScope 魔搭社区上的 32.8B 参数text-generation模型，采用 mit 许可，基于 Qwen/Qwen2.5-32B 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: MiniMax/SynLogic-32B
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 32.8B
- **Base model**: Qwen/Qwen2.5-32B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: LLM
- **Downloads**: 561
- **Stars**: 2
- **Last updated**: 2025-06-10

Source: https://www.modelscope.cn/models/MiniMax/SynLogic-32B

---

# SynLogic-32B: Advanced Logical Reasoning Model

* 🐙 **GitHub Repo:** [https://github.com/MiniMax-AI/SynLogic](https://github.com/MiniMax-AI/SynLogic)
* 📜 **Paper (arXiv):** [https://arxiv.org/abs/2505.19641](https://arxiv.org/abs/2505.19641)
* 🤗 **Dataset:** [SynLogic on Hugging Face](https://huggingface.co/datasets/MiniMaxAI/SynLogic)

## Model Overview

**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.

## Key Features

* **Comprehensive Logical Reasoning:** Trained on 35 diverse logical reasoning tasks including Sudoku, Game of 24, Cipher, Arrow Maze, and more
* **Verifiable Training:** All training data can be automatically verified, enabling effective reinforcement learning
* **Strong Generalization:** Transfers logical reasoning skills to mathematical problem-solving without explicit math training

## Performance Highlights

| Model | BBEH | KOR-Bench | BBH |
|-------|------|-----------|-----|
| 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.

## Training Details

* **Base Model:** Qwen2.5-32B-Base
* **Training Algorithm:** GRPO (Group Relative Policy Optimization)
* **Dataset:** 33k SynLogic-Hard samples with controlled difficulty
* **Reward Design:** Binary rewards based on format adherence and correctness verification

## Citation

```bibtex
@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}, 
}
```