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README.md

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+---
+license: apache-2.0
+task_categories:
+  - visual-question-answering
+  - question-answering
+  - text-generation
+language:
+  - en
+tags:
+  - multimodal
+  - reasoning
+  - chain-of-thought
+  - mathematics
+  - science
+  - STEM
+  - visual-reasoning
+  - VLM
+  - distillation
+size_categories:
+  - 1M<n<10M
+---
+
+<div align="center">
+  <h1>MMFineReason</h1>
+  <p><strong>Closing the Multimodal Reasoning Gap via Open Data-Centric Methods</strong></p>
+</div>
+
+<div align="center">
+
+[![Paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2601.21821)
+[![Homepage](https://img.shields.io/badge/Homepage-MMFineReason-blue)](https://mmfinereason.github.io/)
+[![Collections](https://img.shields.io/badge/🤗-Collections-yellow)](https://huggingface.co/collections/OpenDataArena/mmfinereason)
+
+</div>
+
+<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>
+
+---
+
+## 📖 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
+
+---
+
+## 🏗️ Data Construction Pipeline
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/pipeline_detailed.png" width="100%" alt="Data Construction Pipeline">
+  <figcaption><em>MMFineReason data pipeline and the two-stage training.</em></figcaption>
+</figure>
+
+### Stage 1: Data Collection & Standardization
+
+- Aggregate diverse multimodal datasets from open-source community
+- Translate non-English questions; remove noise and extraneous artifacts
+- Rewrite shallow prompts into reasoning-encouraging instructions
+- Filter non-reasoning tasks; clean corrupted/oversized images
+
+### Stage 2: Reasoning Distillation
+
+- **Teacher Model**: Qwen3-VL-235B-A22B-Thinking
+- **Four-Phase Framework**: Information Extraction → Problem Setup → Solution Execution → Validation
+- **Output**: Reasoning in `<think>...</think>`, final answer in `<answer>...</answer>`
+- **Caption Generation**: 100% coverage via Qwen3-VL-235B-A22B-Thinking
+
+### Stage 3: Data Selection
+
+- **Quality Filtering**: Template/length validation, n-gram deduplication, correctness verification (~20% removed)
+- **Difficulty Filtering**: Use Qwen3-VL-4B-Thinking pass rate as proxy
+  - **MMFineReason-123K**: Pass rate = 0 (hardest 7%)
+  - **MMFineReason-586K**: Pass rate ≠ 1 (challenging 33%)
+
+---
+
+## 🔧 Data Schema
+
+| Field | Description |
+|-------|-------------|
+| `source` | Origin dataset name (e.g., "Geometry3K", "MMR1", "BMMR") |
+| `id` | Unique sample identifier within the source dataset |
+| `original_question` | Raw question text as obtained from the source |
+| `original_answer` | Raw answer as obtained from the source |
+| `image` | Visual input (PIL Image) |
+| `question` | Cleaned, standardized question in English |
+| `answer` | Verified answer extracted and standardized |
+| `qwen3vl_235b_instruct_caption` | Dense visual description generated by Qwen3-VL-235B-A22B-Instruct |
+| `qwen3vl_235b_thinking_response` | Long-form Chain-of-Thought reasoning generated by Qwen3-VL-235B-A22B-Thinking |
+| `qwen3vl_4b_pass_rate` | Difficulty proxy based on Qwen3-VL-4B-Thinking's performance (0.0 = hardest, 1.0 = easiest) |
+| `is_consistent` | Boolean indicating whether generated reasoning matches ground truth |
+| `consistency_analysis` | Detailed analysis of consistency verification |
+
+---
+
+## 🗂️ Dataset Composition
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/composition_sunburst.png" width="100%" alt="Dataset Composition">
+  <figcaption><em>Dataset composition of MMFineReason-1.8M.</em></figcaption>
+</figure>
+
+**Mathematics (79.4%)** forms the backbone, primarily sourced from MMR1 (1.27M) and enriched with WaltonColdStart, ViRL39K, Euclid30K, MMK12, Geo170K, Geo3K, mm-openr1, and the WeMath family.
+
+**Science (13.8%)** is anchored by VisualWebInstruct (157.3K) and BMMR (54.6K), complemented by TQA, AI2D, Zebra-CoT, and ScienceQA.
+
+**Puzzle/Game (4.6%)** targets strategic planning and abstract reasoning, dominated by GameQA-140K (71.7K) and enriched by Raven, VisualSphinx, and PuzzleQA.
+
+**General/OCR (2.2%)** includes 38.7K samples from LLaVA-CoT, serving as regularization to preserve broad visual and OCR capabilities.
+
+---
+
+## 📊 Dataset Statistics
+
+### Token Length Comparison with Other Datasets
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/table_token_length.png" width="100%" alt="Token Length Statistics Comparison">
+  <figcaption><em>Comparison of token length statistics across datasets.</em></figcaption>
+</figure>
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/token_length_distribution.png" width="100%" alt="Token Length Distribution">
+  <figcaption><em>Token length analysis. (Left) Internal domain distribution; (Mid) External CoT comparison; (Right) Caption richness comparison.</em></figcaption>
+</figure>
+
+MMFineReason achieves an average CoT length of **2,910 tokens**—approximately **2.7× longer** than HoneyBee and **4.3× longer** than OpenMMReasoner. The extended tail (Max: 16,316) demonstrates capacity for highly complex, multi-stage reasoning tasks. For captions, MMFineReason averages 609 tokens with **100% coverage**, compared to HoneyBee's 299 tokens at ~58% coverage.
+
+---
+
+### 🖼️ Image Category Distribution
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/table_image_category.png" width="100%" alt="Image Category Statistics">
+  <figcaption><em>Image category statistics by group (STEM vs. Natural).</em></figcaption>
+</figure>
+
+The corpus is predominantly STEM and diagrammatic content (98.3%), with geometric diagrams, mathematical plots, and logic puzzles accounting for 75.3%. Natural images (1.7%) provide diversity across urban scenes, indoor scenes, and human activities for generalization assessment.
+
+---
+
+### 📈 Difficulty Distribution
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/pass_rate_distribution.png" width="100%" alt="Pass Rate Distribution">
+  <figcaption><em>Pass rate distribution across sub-datasets, sorted by descending mean pass rate.</em></figcaption>
+</figure>
+
+Science-oriented datasets (ScienceQA, AI2D, TQA) exhibit high pass rates due to clean diagrams and MCQ format. Puzzle/game datasets (GameQA-140K, Raven, VisualSphinx) show lowest pass rates, requiring multi-step abstract reasoning. The binary distribution pattern reflects that reasoning tasks often follow all-or-nothing outcomes.
+
+---
+
+## 📊 Benchmark Results
+
+### Main Results
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/table_main_results.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/table_sft_rl_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.
+
+---
+
+## 🔬 Ablation Studies
+
+### Data Efficiency ("Less is More")
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/ablation_data_efficiency.png" width="100%" alt="Data Efficiency Analysis">
+  <figcaption><em>Performance comparison across different data scales and model sizes.</em></figcaption>
+</figure>
+
+Removing 67% easy samples (Pass Rate = 1) improves performance by 0.6 points (75.0 → 75.6). Training on only the hardest 7% (123K samples) achieves 73.3—surpassing Qwen3-VL-8B-Thinking (72.5) with 14× less data. This demonstrates that challenging samples provide most training signal, and rigorous filtering eliminates redundancy in large-scale datasets.
+
+### Sub-Dataset Performance
+
+<figure align="center">
+  <img src="https://raw.githubusercontent.com/mmfinereason/mmfinereason.github.io/main/static/images/subdataset_performance.png" width="100%" alt="Sub-Dataset Performance Analysis">
+  <figcaption><em>Performance landscape of distilled sub-datasets (x-axis: sample count, log scale).</em></figcaption>
+</figure>
+
+ViRL39K (39K samples) retains 98.9% of MMR1's (1.5M) performance with only 2.4% data volume. WeMath2.0-SFT achieves 70.98% with just 814 samples, matching datasets 1000× larger. BMMR (80K, 300+ disciplines) outperforms the larger GameQA-140K (140K), showing that disciplinary diversity matters more than scale.
+
+---
+
+## 🏆 Trained Models
+
+| Model | Parameters | Avg Score | HuggingFace |
+|-------|------------|-----------|-------------|
+| MMFineReason-2B | 2B | 65.3 | [🤗 Link](https://huggingface.co/OpenDataArena/MMFineReason-2B) |
+| MMFineReason-4B | 4B | 73.9 | [🤗 Link](https://huggingface.co/OpenDataArena/MMFineReason-4B) |
+| MMFineReason-8B | 8B | 75.7 | [🤗 Link](https://huggingface.co/OpenDataArena/MMFineReason-8B) |
+
+---
+
+## 📚 Citation
+
+```bibtex
+@misc{lin2026mmfinereasonclosingmultimodalreasoning,
+      title={MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods}, 
+      author={Honglin Lin and Zheng Liu and Yun Zhu and Chonghan Qin and Juekai Lin and Xiaoran Shang and Conghui He and Wentao Zhang and Lijun Wu},
+      year={2026},
+      eprint={2601.21821},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.21821}, 
+}
+```
+
+---
+
+## 📄 License
+
+This dataset is released under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0). 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.

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