Human motion understanding and generation are crucial for vision and robotics but remain limited in reasoning capability and test-time planning. We propose MoRL, a unified multimodal motion model trained with supervised fine-tuning and reinforcement learning with verifiable rewards. Our task-specific reward design combines semantic alignment and reasoning coherence for understanding with physical plausibility and text-motion consistency for generation, improving both logical reasoning and perceptual realism. To further enhance inference, we introduce Chain-of-Motion (CoM), a test-time reasoning method that enables step-by-step planning and reflection. We also construct two large-scale CoT datasets, MoUnd-CoT-140K and MoGen-CoT-140K, to align motion sequences with reasoning traces and action descriptions. Experiments on HumanML3D and KIT-ML show that MoRL achieves significant gains over state-of-the-art baselines. Code: https://github.com/AIGeeksGroup/MoRL. Website: https://aigeeksgroup.github.io/MoRL.

翻译：人体运动理解与生成是视觉与机器人领域的核心课题，但在推理能力与测试时规划方面仍存在局限。本文提出MoRL——一种通过监督微调与可验证奖励强化学习训练的统一多模态运动模型。我们针对特定任务设计的奖励机制融合了语义对齐与推理连贯性以提升理解能力，结合物理合理性与文本-运动一致性以优化生成质量，从而同步增强逻辑推理与感知真实性。为强化推理过程，我们提出链式运动推理方法，该测试时推理机制支持分步规划与反思。同时构建了两个大规模思维链数据集MoUnd-CoT-140K与MoGen-CoT-140K，将运动序列与推理轨迹及动作描述进行对齐。在HumanML3D和KIT-ML数据集上的实验表明，MoRL相较现有最优基线模型取得显著性能提升。代码：https://github.com/AIGeeksGroup/MoRL。项目网站：https://aigeeksgroup.github.io/MoRL。