---
title: PixelReasoner-SFT-Data
canonical_url: "https://www.modelscope.cn/datasets/TIGER-Lab/PixelReasoner-SFT-Data"
md_url: "https://www.modelscope.cn/datasets/TIGER-Lab/PixelReasoner-SFT-Data.md"
repository: TIGER-Lab/PixelReasoner-SFT-Data
last_updated: 2025-08-13
license: apache-2.0
storage_size: "4.2 GB"
downloads: 828
stars: 0
---

# PixelReasoner-SFT-Data

> PixelReasoner-SFT-Data - TIGER-Lab 在 ModelScope 开源的数据集。Overview. The SFT data for training Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning, The queries require fine-grained visual analysis in both images…

TIGER-Lab/PixelReasoner-SFT-Data 是 ModelScope 魔搭社区上的数据集，存储大小 4.2 GB，采用 apache-2.0 许可。

- **Repository**: TIGER-Lab/PixelReasoner-SFT-Data
- **License**: apache-2.0
- **Storage size**: 4.2 GB
- **Downloads**: 828
- **Stars**: 0
- **Last updated**: 2025-08-13

Source: https://www.modelscope.cn/datasets/TIGER-Lab/PixelReasoner-SFT-Data

---

**Overview.**
The SFT data for training [**Pixel Reasoner**: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning](https://arxiv.org/abs/2505.15966),
The queries require fine-grained visual analysis in both images (e.g., infographics, visually-rich scenes, etc) and videos. 

**Details.**
The data contains 8,000+ reasoning trajectories, including :
- 2,000+ textual reasoning trajectories, rejection sampled from the base model Qwen2.5-VL-Instruct. These data aims to preserve textual reasoning ability on easier VL queries.
- 6,000+ pixel-space reasoning trajectories synthesized using GPT-4o. These include single-pass trajectories and error-induced self-correction trajectories, on both image and video inputs. 

**Update:** The initial data has merged the textual reasoning and pixel-space reasoning to train adaptive use of different reasoning modes. The separate files for each reasoning mode is now uploaded.

Each trajectory is stored as message list, following Qwen's message template. 

The data is structured according to the SFT code. 

**Note**: Remember to unzip `images.zip` and `videos.zip`, and replace the relative path with the absolute path in `image` and `video` key of the message entries.

**Training Code**: The SFT code can be found at https://github.com/TIGER-AI-Lab/Pixel-Reasoner/tree/main

**Project page**: https://tiger-ai-lab.github.io/Pixel-Reasoner/