Abstract
We open-source \textbf{MiMo-VL-Miloco-7B} and its quantized variant \textbf{MiMo-VL-Miloco-7B-GGUF}, a pair of home-centric vision-language models that achieve strong performance on both home-scenario understanding and general multimodal reasoning. Built on the MiMo-VL-7B backbone, MiMo-VL-Miloco-7B is specialized for smart-home environments, attaining leading F1 scores on gesture recognition and common home-scenario understanding, while also delivering consistent gains across video benchmarks such as Video-MME, Video-MMMU, and Charades-STA, as well as language understanding benchmarks including MMMU-Pro and MMLU-Pro. In our experiments, MiMo-VL-Miloco-7B outperforms strong closed-source and open-source baselines on home-scenario understanding and several multimodal reasoning benchmarks. To balance specialization and generality, we design a two-stage training pipeline that combines supervised fine-tuning with reinforcement learning based on Group Relative Policy Optimization, leveraging efficient multi-domain data. We further incorporate chain-of-thought supervision and token-budget-aware reasoning, enabling the model to learn knowledge in a data-efficient manner while also performing reasoning efficiently. Our analysis shows that targeted home-scenario training not only enhances activity and gesture understanding, but also improves text-only reasoning with only modest trade-offs on document-centric tasks. Model checkpoints, quantized GGUF weights, and our home-scenario evaluation toolkit are publicly available at \href{this https URL}{this https URL} to support research and deployment in real-world smart-home applications.
Abstract (translated)
我们开源了**MiMo-VL-Miloco-7B**及其量化版本**MiMo-VL-Miloco-7B-GGUF**，这是一对专注于家庭环境的视觉语言模型，在家用场景理解和通用多模态推理方面均表现出色。基于MiMo-VL-7B骨干网络，MiMo-VL-Miloco-7B专门针对智能家庭环境进行了优化，在手势识别和常见家庭情景理解上取得了领先的F1分数，并且在Video-MME、Video-MMMU、Charades-STA等视频基准测试以及MMMU-Pro和MMLU-Pro等语言理解基准测试中也表现出一致的优势。实验表明，MiMo-VL-Miloco-7B在家用场景理解和多项多模态推理基准测试上均超越了强大的闭源和开源基线模型。 为了平衡专业性和通用性，我们设计了一个两阶段的训练流程，结合监督微调与基于Group Relative Policy Optimization（GRPO）的强化学习方法，并利用高效的跨域数据。此外，我们进一步引入了链式思维监督及令牌预算感知推理机制，使得模型能够在高效的数据利用率下学习知识的同时，还能够进行有效的推理过程。 我们的分析表明，针对家庭情景的定向训练不仅增强了活动和手势的理解能力，而且还适度改善了仅限于文本的任务上的推理性能。模型检查点、量化GGUF权重以及我们家用场景评估工具包已公开发布在[此链接](this https URL)上，以支持研究与实际智能家庭应用中的部署工作。
URL
https://arxiv.org/abs/2512.17436
