@inproceedings{guo-etal-2025-mammoth,
title = "{MA}mmo{TH}-{VL}: Eliciting Multimodal Reasoning with Instruction Tuning at Scale",
author = "Guo, Jiawei and
Zheng, Tianyu and
Li, Yizhi and
Bai, Yuelin and
Li, Bo and
Wang, Yubo and
Zhu, King and
Neubig, Graham and
Chen, Wenhu and
Yue, Xiang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.680/",
doi = "10.18653/v1/2025.acl-long.680",
pages = "13869--13920",
ISBN = "979-8-89176-251-0",
abstract = "Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of 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.Experiments demonstrate that training MLLMs on our dataset not only significantly improves reasoning capabilities, achieving state-of-the-art performance on benchmarks such as MathVerse (+8.1{\%}), MMMU-Pro (+7{\%}), and MuirBench (+13.3{\%}), but also gains improvements of up to 4{\%} on non-reasoning-based benchmarks."
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<abstract>Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of 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.Experiments demonstrate that training MLLMs on our dataset not only significantly improves reasoning capabilities, achieving state-of-the-art performance on benchmarks such as MathVerse (+8.1%), MMMU-Pro (+7%), and MuirBench (+13.3%), but also gains improvements of up to 4% on non-reasoning-based benchmarks.</abstract>
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%0 Conference Proceedings
%T MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale
%A Guo, Jiawei
%A Zheng, Tianyu
%A Li, Yizhi
%A Bai, Yuelin
%A Li, Bo
%A Wang, Yubo
%A Zhu, King
%A Neubig, Graham
%A Chen, Wenhu
%A Yue, Xiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F guo-etal-2025-mammoth
%X Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of 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.Experiments demonstrate that training MLLMs on our dataset not only significantly improves reasoning capabilities, achieving state-of-the-art performance on benchmarks such as MathVerse (+8.1%), MMMU-Pro (+7%), and MuirBench (+13.3%), but also gains improvements of up to 4% on non-reasoning-based benchmarks.
%R 10.18653/v1/2025.acl-long.680
%U https://aclanthology.org/2025.acl-long.680/
%U https://doi.org/10.18653/v1/2025.acl-long.680
%P 13869-13920
Markdown (Informal)
[MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale](https://aclanthology.org/2025.acl-long.680/) (Guo et al., ACL 2025)
ACL
- Jiawei Guo, Tianyu Zheng, Yizhi Li, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Graham Neubig, Wenhu Chen, and Xiang Yue. 2025. MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13869–13920, Vienna, Austria. Association for Computational Linguistics.