MIMO-VL-7B: A powerful visual language model that enhances general visual understanding and multimodal reasoning
Visual Language Model (VLM) has become a fundamental component of multimodal AI systems, enabling autonomous agents to understand the visual environment, the reasons for multimodal content, and interact with the digital and physical worlds. The importance of these capabilities has led to extensive research across architectural design and training methods, resulting in rapid progress on the site. Xiaomi researchers introduced the MIMO-VL-7B, a compact and powerful VLM that includes three key components: a natural resolution vision transformer encoder that retains fine-grained visual details, a multi-layer observation projector, an efficient cross-model and MIMO-7B language model to implement the MIMO-7B language model to implement the Mimo-7b language model.Strike mission.
MIMO-VL-7B undergoes two sequential training processes. The first process is a four-stage pre-training phase that includes projector warm-up, visual alignment, general multi-mode pre-training and long-form cultural supervision fine-tuning, which consumes 2.4 trillion tokens of high-quality data sets. This results in the MIMO-VL-7B-SFT model. The second process is the post-training phase, which introduces mixed loss reinforcement learning (MORL), integrating diverse reward signals across perceptual accuracy, visual grounding accuracy, logical reasoning capabilities, and human preferences. This produces the MIMO-VL-7B-RL model. The main findings show that incorporating high-quality wide coverage inference data from the training phase can enhance model performance while achieving stability while improving is still challenging.
The MIMO-VL-7B architecture consists of three components, (a) a visual transformer (VIT) for encoding visual inputs such as images and videos, (b) a projector, mapping visual encoding into potential space consistent with LLM, and (c) the LLM itself for text understanding and reasoning. QWEN2.5-VIT is used as a visual encoder that supports native resolution inputs. The LLM main chain with MIMO-7B basics serves as its strong inference capability, and a randomly initialized multi-layer perceptron (MLP) as a projector for the architecture of the model. The model’s pre-trained dataset contains 2.4 trillion tokens, multiple mode data, image titles, interleaved data, optical character recognition (OCR) data, grounded data, video content, GUI interaction, inference examples, and text sequences.
The post-training phase further enhances MIMO-VL-7B on challenging inference tasks and is consistent through the use of the MORL framework by seamlessly integrating reinforcement learning with verifiable rewards (RLVR) and RLHF and consistently through human preferences. RLVR uses rule-based rewards for continuous self-improvement, so multiple verifiable inference and perception tasks are designed to accurately verify the final answer using predefined rules. RLHF is used in this verifiable reward framework to address human preference consistency and mitigate adverse behaviors. In addition, MORL is implemented to optimize both RLVR and RLHF targets simultaneously.
A comprehensive evaluation across 50 tasks demonstrates the state-of-the-art performance of MIMO-VL-7B in an open source model. Overall, the model achieved remarkable results on general vision tasks, with MIMO-VL-7B-SFT and MIMO-VL-7B-RL achieving 64.6% and 66.7% of MMMUValrespectively, surpassing larger models such as Gemma 3 27B. To understand the documentation, the MIMO-VL-7B-RL is excellent at 56.5% on CharXiVRQ, significantly surpassing QWEN2.5-VL 14.0 points, and InternVL3 times 18.9 points. Both RL and SFT models are significantly better than open source baselines in multimodal inference tasks, with MIMO-VL-7B-SFT even surpassing larger models, including QWEN2.5-VL-72B and QVQ-72B-72B preview. The RL variant can achieve further improvements, increasing mathematical accuracy from 57.9% to 60.4%.
MIMO-VL-7B demonstrates excellent GUI understanding and grounding capabilities, the RL model outperforms all general VLMs and compares comparable or excellent performance with GUI-specific models on GUI professional models such as ScreenSpot-Pro and Osworld-g (e.g. ScreenSpot-Pro and Osworld-g). The model achieves the highest ELO rating among all the evaluated open source VLMs, ranks first among models spanning 7B to 72B parameters, and is close to proprietary models such as Claude 3.7 sonnets. Morl provides a significant 22+ point improvement for the SFT model, thus verifying the effectiveness of the training method and highlighting the competitiveness of this general VLM method.
In summary, the researchers introduced the MIMO-VL-7B model that achieves state-of-the-art performance through curated, high-quality pre-trained datasets and MORL frameworks. Key development insights include consistent growth in incorporating inference data at a later stage of training, the advantages of policy RL in training over vanilla GRPO, and task interference challenges when applying MORL to diversity functions. Researchers open a comprehensive evaluation kit to promote transparency and repeatability in multimodal studies. This work drives competent open source visual language models and provides valuable insights to the community.
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Sajjad Ansari is a final year undergraduate student from IIT Kharagpur. As a technology enthusiast, he delves into the practical application of AI, focusing on understanding AI technology and its real-world impact. He aims to express complex AI concepts in a clear and easy way.
