Recent advancements in Multimodal Large Language Models (MLLMs), particularly through Reinforcement Learning with Verifiable Rewards (RLVR), have significantly enhanced their reasoning abilities. However, a critical gap persists: these models struggle with dynamic spatial interactions, a capability essential for real-world applications. To bridge this gap, we introduce M2-Reasoning-7B, a model designed to excel in both general and spatial reasoning. Our approach integrates two key innovations: (1) a novel data pipeline that generates 294.2K high-quality data samples (168K for cold-start fine-tuning and 126.2K for RLVR), which feature logically coherent reasoning trajectories and have undergone comprehensive assessment; and (2) a dynamic multi-task training strategy with step-wise optimization to mitigate conflicts between data, and task-specific rewards for delivering tailored incentive signals. This combination of curated data and advanced training allows M2-Reasoning-7B to set a new state-of-the-art (SOTA) across 8 benchmarks, showcasing superior performance in both general and spatial reasoning domains.
@article{arxiv.2507.08306,
title = {M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning},
author = {Inclusion AI and : and Fudong Wang and Jiajia Liu and Jingdong Chen and Jun Zhou and Kaixiang Ji and Lixiang Ru and Qingpei Guo and Ruobing Zheng and Tianqi Li and Yi Yuan and Yifan Mao and Yuting Xiao and Ziping Ma},
journal= {arXiv preprint arXiv:2507.08306},
year = {2025}
}