Paper 2601.21821
MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
- Published
- Jan 2026
- Research lab
- Independent
- Citations
- 33
- GitHub
- 6 stars
01 In brief
Summary
MMFineReason is a large-scale multimodal reasoning dataset with 1.8M samples and 5.1B solution tokens, built via a three-stage pipeline: data aggregation and standardization, CoT distillation from Qwen3-VL-235B-A22B-Thinking, and quality/difficulty-based selection.
The dataset covers mathematics (79.4%), science (13.8%), puzzle/game (4.6%), and general/OCR (2.2%), with an average CoT length of 2,910 tokens.
Fine-tuning Qwen3-VL-Instruct on this data yields MMFineReason-2B/4B/8B models that set SOTA results for their size: MMFineReason-4B surpasses Qwen3-VL-8B-Thinking, and MMFineReason-8B outperforms Qwen3-VL-30B-A3B-Thinking while approaching Qwen3-VL-32B-Thinking.
A difficulty-aware filtering strategy reveals a 'less is more' effect: a 123K subset (7% of data) achieves performance comparable to the full dataset.
Ablations show that reasoning-oriented data composition improves general capabilities, ultra-high resolution (2048²) offers diminishing returns for reasoning, and caption augmentation provides marginal benefit once CoTs are mature.
The dataset and models are released open-source.
02 From the paper
Abstract
Recent advances in Vision Language Models (VLMs) have driven significant progress in visual reasoning. However, open-source VLMs still lag behind proprietary systems, largely due to the lack of high-quality reasoning data. Existing datasets offer limited coverage of challenging domains such as STEM diagrams and visual puzzles, and lack consistent, long-form Chain-of-Thought (CoT) annotations essential for eliciting strong reasoning capabilities. To bridge this gap, we introduce MMFineReason, a large-scale multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring high-quality reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking. The dataset is established via a systematic three-stage pipeline: (1) large-scale data collection and standardization, (2) CoT rationale generation, and (3) comprehensive selection based on reasoning quality and difficulty awareness. The resulting dataset spans STEM problems, visual puzzles, games, and complex diagrams, with each sample annotated with visually grounded reasoning traces. We fine-tune Qwen3-VL-Instruct on MMFineReason to develop MMFineReason-2B/4B/8B versions. Our models establish new state-of-the-art results for their size class. Notably, MMFineReason-4B succesfully surpasses Qwen3-VL-8B-Thinking, and MMFineReason-8B even outperforms Qwen3-VL-30B-A3B-Thinking while approaching Qwen3-VL-32B-Thinking, demonstrating remarkable parameter efficiency. Crucially, we uncover a "less is more" phenomenon via our difficulty-aware filtering strategy: a subset of just 7\% (123K samples) achieves performance comparable to the full dataset. Notably, we reveal a synergistic effect where reasoning-oriented data composition simultaneously boosts general capabilities.