OpenDataArena/MMFineReason-2B

<div align="center"> <h1>MMFineReason</h1> <p><strong>Closing the Multimodal Reasoning Gap via Open Data-Centric Methods</strong></p> </div>

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<figure align="center"> <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/model_compare.png" width="100%" alt="Model Performance Comparison"> <figcaption><em>Average score across mathematical reasoning and multimodal understanding benchmarks.</em></figcaption> </figure>

This repository provides MMFineReason-2B; detailed dataset information is available at https://huggingface.co/datasets/OpenDataArena/MMFineReason-1.8M.

📖 Overview

MMFineReason is a large-scale, high-quality multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring detailed reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking.

🎯 Key Highlights

• —1.8M High-Quality Samples with 5.1B Solution Tokens
• —Long-Form CoT: Average reasoning length of 2,910 tokens (2.7× HoneyBee, 4.3× OpenMMReasoner)
• —100% Caption Coverage: Dense visual descriptions averaging 609 tokens
• —Multi-Domain: Mathematics (79.4%), Science (13.8%), Puzzle/Game (4.6%), General/OCR (2.2%)
• —State-of-the-Art: Models trained on this dataset achieve SOTA performance in their size class

🧠 Model Training

Based on the MMFineReason dataset, we train a family of multimodal reasoning models at 2B / 4B / 8B scales, all initialized from the corresponding Qwen3-VL-Instruct backbones and fine-tuned using a unified data-centric training recipe.

Each MMFineReason model is trained in two stages:

• —Supervised Fine-Tuning (SFT) on MMFineReason-1.8M-SFT, leveraging long-form, visually grounded Chain-of-Thought (CoT) annotations with an average length of 2,910 tokens.

• —Reinforcement Learning (RL) using GSPO, applied on MMFineReason-1.8M-RL to further improve reasoning reliability and generalization.

📊 Model Performance

Main Results

<figure align="center"> <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/tablemainresults.png" width="100%" alt="Main Benchmark Results"> <figcaption><em>Comparison of MMFineReason models with state-of-the-art models.</em></figcaption> </figure>

MMFineReason-4B surpasses Qwen3-VL-8B-Thinking (73.9 vs 72.5), while MMFineReason-8B outperforms the larger Qwen3-VL-30B-A3B-Thinking (75.7 vs 74.5) and exceeds Gemini-2.5-Flash. On mathematical benchmarks, MFR-8B achieves 83.4% on DynaMath (vs Qwen3-VL-32B-Thinking's 82.0%) and 67.1% on MathVision, outperforming HoneyBee-8B and OMR-7B by 23-30 points. Despite minimal chart training data, MFR-8B generalizes well to CharXiv (90.8%) and RealWorldQA (75.6%).

SFT vs RL Training Analysis

<figure align="center"> <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/tablesftrl_results.png" width="100%" alt="SFT vs RL Results"> <figcaption><em>Results comparing MFR-SFT and MFR-Thinking models against base Qwen3-VL variants.</em></figcaption> </figure>

SFT drives major gains in mathematical reasoning (e.g., MathVision: 53.9% → 67.6% for 8B). RL enhances generalization on understanding benchmarks (e.g., AI2D: 78.5% → 82.5% for 2B) while showing variance on math benchmarks.

🏆 Model Zoo

📚 Citation

@article{lin2026mmfinereason,
  title={MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods},
  author={Lin, Honglin and Liu, Zheng and Zhu, Yun and Qin, Chonghan and Lin, Juekai and Shang, Xiaoran and He, Conghui and Zhang, Wentao and Wu, Lijun},
  journal={arXiv preprint arXiv:2601.21821},
  year={2026},
  url={https://mmfinereason.github.io/}
}

📄 License

This dataset is released under the Apache 2.0 License. Individual source datasets may have their own licenses.

🤝 Acknowledgments

We thank the creators of FineVision, MMR1, BMMR, Euclid30K, GameQA-140K, LLaVA-CoT, WeMath, ViRL39K, and others. We also thank the Qwen team for the powerful Qwen3-VL series models.