MMR1-Math-V0-7B model and MMR1-MMR-MMR-DAT-DATA-V0 DATA SET

Advances in multimodal large language models have improved the ai ​​ability to interpret and reason on complex visual and textual information. Despite these improvements, the field faces persistent challenges, especially in mathematical reasoning tasks. Traditional multimodal ai systems, even those with extensive training data and large parameter counts, often fight to interpret and solve mathematical problems that involve visual contexts or geometric configurations. These limitations highlight the urgent need of specialized models capable of analyzing complex multimodal mathematical problems with greater precision, efficiency and sophistication of reasoning.

Researchers from the Technological University of Nanyang (NTU) introduced the MMR1-Math-V0-7B model and the specialized MMR1-MMR-RL-DATA-V0 data set To address the previous critical challenges. This pioneering model is explicitly adapts to mathematical reasoning within multimodal tasks, showing remarkable efficiency and avant -garde performance. MMR1-Math-V0-7B is distinguished from previous multimodal models due to its ability to achieve leading performance using a remarkably minimal training data set, thus redefining reference points within this domain.

The model has been adjusted using only 6,000 meticulously selected data samples of public access data sets. The researchers applied a balanced data selection strategy, emphasizing uniformity in terms of difficulty for problems and diversity of mathematical reasoning. By systematically filtering too simplistic problems, NTU researchers assured that the training data set understood problems that effectively challenged and improved the model's reasoning capabilities.

The MMR1-Math-V0-7B architecture is based on the QWEN2.5-VL multimodal column and refine even more using a new training method known as optimization of policies driven by generalized rewards (GRPO). Take advantage of GRPO allowed researchers to efficiently train the model in a reinforcement learning configuration for more than 15 times, taking approximately six hours in 64 GPU H100 NVIDIA. The relatively short training period and the efficient use of computational resources underline the impressive capacity of the model for rapid assimilation and generalization of knowledge.

MMR1-Math-V0-7B was evaluated against established reference points using the standardized VLMEVALKIT, focusing on multimodal mathematical reasoning tasks. The reference points included Mathvista_mini, Mathvision, Logicvista and Mathverse_mini. MMR1-Math-V0-7B delivered innovative results, overcoming the existing 7B models of open source and rivaling even patented models with significantly larger parameters.

In particular, the model reached 71.0%precision in Mathvista, exceeding notable counterparts such as QWEN2.5-VL (68.2%) and LMM-R1 (63.2%). In Mathvision, MMR1-Math-V0-7B obtained 30.2%, especially exceeding other prominent models in the same kind of parameters. In addition, in Logicvista and Mathverse, the model recorded yield figures of 50.8% and 45.1%, respectively, higher than almost all comparable models. These results highlight the exceptional generalization of MMR1-Math-V0-7B and multimodal reasoning skill in mathematical contexts.

Several key conclusions of this launch include:

• The MMR1-Math-V0-7B model, developed by NTU researchers, establishes a new latest generation reference point for multimodal mathematical reasoning between 7B parameter models of open source.
• It achieves higher performance using an exceptionally small training set of only 6,000 meticulously selected multimodal samples.
• After 6 hours of training in 64 NVIDIA H100 GPU, an efficient reinforcement learning method (GRPO) works in a robust way.
• The complementary data set of MMR1-Math-RL-Data-V0, which includes 5,780 multimodal mathematics problems, guarantees diverse, balanced and challenging content for models training.
• It exceeds other prominent multimodal models through standard reference points, which demonstrates exceptional efficiency, generalization and reasoning capacity in complex mathematical scenarios.

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