MMFineReason

Closing the Multimodal Reasoning Gap via Open Data-Centric Methods

Model Performance Comparison — *Average score across mathematical reasoning and multimodal understanding benchmarks.*

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

Main Benchmark Results — *Comparison of MMFineReason models with state-of-the-art models.*

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

SFT vs RL Results — *Results comparing MFR-SFT and MFR-Thinking models against base Qwen3-VL variants.*

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

Model	Parameters	Avg Score	HuggingFace
MMFineReason-2B	2B	65.3	🤗 Link
MMFineReason-4B	4B	73.9	🤗 Link
MMFineReason-8B	8B	75.7	🤗 Link

📚 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.