Papers › MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale
Jarvis Guo, Tuney Zheng, Yuelin Bai, Bo Li, YuBo Wang, King Zhu, Yizhi Li, Graham Neubig, Wenhu Chen, Xiang Yue
Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained by existing instruction-tuning datasets, which were predominately repurposed from academic datasets such as VQA, AI2D, and ChartQA. These datasets target simplistic tasks, and only provide phrase-level answers without any intermediate rationales. To address these challenges, we introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales designed to elicit CoT reasoning. Using only open models, we create a dataset containing 12M instruction-response pairs to cover diverse, reasoning-intensive tasks with detailed and faithful rationales. Experiments demonstrate that training MLLMs on this dataset significantly improves reasoning capabilities, achieving state-of-the-art performance on benchmarks such as MathVerse (+8.1%), MMMU-Pro (+7%), and MuirBench (+13.3%). Additionally, the model demonstrates notable improvements of up to 4% on non-reasoning-based benchmarks. Ablation studies further highlight the importance of key components, such as rewriting and self-filtering, in the dataset construction process.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering | MM-Vet | MAmmoTH-VL-8B | GPT-4 score | 62.3 | #31 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | MAmmoTH-VL-8B (SI) | GPT-4 score | 60.6 | #37 of 231 | Archive leaderboard | report |
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