让多模态大模型同时具备通用与空间推理能力
M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning
- 构建高质量数据集，含29.42万条逻辑连贯的推理样本
- 在8个基准上达到新SOTA，空间推理能力显著提升
- 适合需要复杂环境理解的智能体、机器人应用
近期基于可验证奖励强化学习（RLVR）的多模态大语言模型（MLLM）在推理能力上取得显著进展，但仍面临动态空间交互能力不足的问题。为此，我们提出M2-Reasoning-7B模型，兼顾通用与空间推理。方法包括：(1) 构建全新数据流水线，生成294.2万条高质量样本（其中168万用于冷启动微调，126.2万用于RLVR），包含逻辑连贯的推理轨迹并经全面评估；(2) 采用动态多任务训练策略，分步优化以缓解任务冲突，并设计任务专属奖励信号。该组合使模型在8个基准上达到新SOTA，显著提升通用与空间推理表现。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。