  Multi-modal Large Language Models (MLLMs) have recently emerged as a
significant focus in academia and industry. Despite their proficiency in
general multi-modal scenarios, the mathematical problem-solving capabilities in
visual contexts remain insufficiently explored. We identify three key areas
within MLLMs that need to be improved: visual encoding of math diagrams,
diagram-language alignment, and mathematical reasoning skills. This draws forth
an urgent demand for large-scale, high-quality data and training pipelines in
visual mathematics. In this paper, we propose MAVIS, the first MAthematical
VISual instruction tuning paradigm for MLLMs, involving a series of
mathematical visual datasets and specialized MLLMs. Targeting the three issues,
MAVIS contains three progressive training stages from scratch. First, we curate
MAVIS-Caption, consisting of 558K diagram-caption pairs, to fine-tune a
math-specific vision encoder (CLIP-Math) through contrastive learning, tailored
for improved diagram visual encoding. Second, we utilize MAVIS-Caption to align
the CLIP-Math with a large language model (LLM) by a projection layer,
enhancing vision-language alignment in mathematical domains. Third, we
introduce MAVIS-Instruct, including 900K meticulously collected and annotated
visual math problems, which is adopted to finally instruct-tune the MLLM for
robust mathematical reasoning skills. In MAVIS-Instruct, we incorporate
complete chain-of-thought (CoT) rationales for each problem, and minimize
textual redundancy, thereby concentrating the model towards the visual
elements. Data and Models are released at https://github.com/ZrrSkywalker/MAVIS
