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README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+datasets:
+- tanhuajie2001/Reason-RFT-CoT-Dataset
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
+---
+
+<div align="center">
+<img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/logo.png" width="500"/>
+</div>
+
+# 🤗 Reason-RFT CoT Dateset
+*The model checkpoints in our project "Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning"*.
+
+
+<p align="center">
+    </a>&nbsp&nbsp⭐️ <a href="https://tanhuajie.github.io/ReasonRFT/">Project</a></a>&nbsp&nbsp │ &nbsp&nbsp🌎 <a href="https://github.com/tanhuajie/Reason-RFT">Github</a>&nbsp&nbsp │ &nbsp&nbsp🔥 <a href="https://huggingface.co/datasets/tanhuajie2001/Reason-RFT-CoT-Dataset">Dataset</a>&nbsp&nbsp │ &nbsp&nbsp📑 <a href="https://arxiv.org/abs/2503.20752">ArXiv</a>&nbsp&nbsp │ &nbsp&nbsp💬 <a href="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/wechat.png">WeChat</a>
+</p>
+
+<p align="center">
+</a>&nbsp&nbsp🤖 <a href="https://github.com/FlagOpen/RoboBrain/">RoboBrain</a>: Aim to Explore ReasonRFT Paradigm to Enhance RoboBrain's Embodied Reasoning Capabilities.
+</p>
+
+## ♣️ Model List
+
+| Tasks                  | Reason-RFT-Zero-2B        | Reason-RFT-Zero-7B       | Reason-RFT-2B        | Reason-RFT-7B             |
+|------------------------|---------------------------|---------------------|---------------------------|---------------------------|
+| Visual Counting        | [🤗VC-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Visual-Counting-Qwen2-VL-2B) | [🤗VC-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Visual-Counting-Qwen2-VL-7B) | [🤗VC-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Visual-Counting-Qwen2-VL-2B) | [🤗VC-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Visual-Counting-Qwen2-VL-7B) |
+| Structure Perception   | [🤗SP-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Structure-Perception-Qwen2-VL-2B) | [🤗SP-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Structure-Perception-Qwen2-VL-7B) | [🤗SP-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Structure-Perception-Qwen2-VL-2B) | [🤗SP-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Structure-Perception-Qwen2-VL-7B) |
+| Spatial Transformation | [🤗ST-GRPO-Zero-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Spatial-Transformation-Qwen2-VL-2B) | [🤗ST-GRPO-Zero-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Zero-Spatial-Transformation-Qwen2-VL-7B) | [🤗ST-GRPO-2B](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-2B) | [🤗ST-GRPO-7B](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-7B) | 
+| ***Embodied Tasks***   | 🤖 *Stay Turned*   | 🤖 *Stay Turned*   | 🤖 *Stay Turned*   | 🤖 *Stay Turned*  | 
+
+
+## 🔥 Overview
+Visual reasoning abilities play a crucial role in understanding complex multimodal data, advancing both domain-specific applications and artificial general intelligence (AGI).
+Existing methods improve VLM reasoning via Chain-of-Thought (CoT) supervised fine-tuning, using meticulously annotated training data to enhance visual reasoning capabilities.
+However, this training paradigm may lead to overfitting and cognitive rigidity, restricting the model's ability to transfer visual reasoning skills across domains and limiting its real-world applicability.
+To address these limitations, we propose **Reason-RFT**, a novel reinforcement fine-tuning framework that significantly enhances generalization capabilities in visual reasoning tasks.
+**Reason-RFT** introduces a two-phase training framework for visual reasoning: (1) Supervised Fine-Tuning (SFT) with curated Chain-of-Thought (CoT) data activates the reasoning potential of Vision-Language Models (VLMs), followed by (2) Group Relative Policy Optimization (GRPO)-based reinforcement learning that generates multiple reasoning-response pairs, significantly enhancing generalization in visual reasoning tasks.
+To evaluate **Reason-RFT**'s visual reasoning capabilities, we reconstructed a comprehensive dataset spanning visual counting, structure perception, and spatial transformation, serving as a benchmark to systematically assess visual cognition, geometric understanding, and spatial generalization.
+Experimental results demonstrate Reasoning-RFT's three key advantages: **(1) Performance Enhancement**: achieving state-of-the-art results across multiple tasks, outperforming most mainstream open-source and proprietary models; 
+**(2) Generalization Superiority**: consistently maintaining robust performance across diverse tasks and domains, outperforming alternative training paradigms; 
+**(3) Data Efficiency**: excelling in few-shot learning scenarios while surpassing full-dataset SFT baselines; 
+**Reason-RFT** introduces a novel paradigm in visual reasoning, significantly advancing multimodal research.
+
+<div align="center">
+<img src="https://github.com/tanhuajie/Reason-RFT/raw/main/assets/overview.png" />
+</div>
+
+## 🗞️ News
+
+- **`2025-04-12`**: ⭐️ We released our [Models](https://huggingface.co/tanhuajie2001/Reason-RFT-Spatial-Transformation-Qwen2-VL-2B) to huggingface for [General Visual Reasoning Tasks](#GeneralVisualTasks).
+- **`2025-04-04`**: 🤗 We released our [datasets](https://huggingface.co/datasets/tanhuajie2001/Reason-RFT-CoT-Dataset/) to huggingface for [General Visual Reasoning Tasks](#GeneralVisualTasks).
+- **`2025-04-02`**: 🔥 We released codes and scripts for training/evaluation on [General Visual Reasoning Tasks](#GeneralVisualTasks).
+- **`2025-03-29`**: 🌍 We released the [repository](https://github.com/tanhuajie/Reason-RFT/) and [roadmap](#RoadMap) for **Reason-RFT**.
+- **`2025-03-26`**: 📑 We released our initial [ArXiv paper](https://arxiv.org/abs/2503.20752/) of **Reason-RFT**.
+
+
+## ⭐️ Usage
+
+*Please refer to [Reason-RFT](https://github.com/tanhuajie/Reason-RFT) for more details.*
+
+## 📑 Citation
+If you find this project useful, welcome to cite us.
+```bib
+@article{tan2025reason,
+  title={Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning},
+  author={Tan, Huajie and Ji, Yuheng and Hao, Xiaoshuai and Lin, Minglan and Wang, Pengwei and Wang, Zhongyuan and Zhang, Shanghang},
+  journal={arXiv preprint arXiv:2503.20752},
+  year={2025}
+}
+```