A Survey on Post-Training of Multimodal Large Language Models

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Abstract
The technical evolution of Multimodal Large Language Models (MLLMs) has profoundly influenced the AI community, reshaping how intelligent systems understand, reason, and interact in both the digital and physical worlds. Through large-scale multimodal pretraining, MLLMs lay the foundation for general perception and alignment, while it remains unclear how such models can be transformed into human-grounded behaviors. Multimodal Post-Training, which further refines pretrained MLLMs to align their behaviors with human intent and real-world task demands, has become an important paradigm for advancing multimodal intelligence. Accordingly, both academia and industry have devoted increasing efforts to this direction, pushing the boundaries of multimodal general intelligence at an unprecedented pace. In this survey, we systematically review existing MMPoT research from a behavior-shaping perspective. Guided by this view, we introduce a unified framework that organizes existing MLLMs post-training methods into five major families: instruction following, preference calibration, reasoning enhancement, domain adaptation, and scalable learning. Moreover, we revisit benchmarks and evaluation protocols to critically assess the capabilities and limitations of post-trained MLLMs. To conclude, we provide a synthesized outlook on broader open questions, and highlight future research directions toward more general and reliable multimodal capabilities. An updated paper list is available at: https://github.com/zchoi/Awesome-post-training-for-MLLMs.

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