标题： ReasonAct：小模型中细粒度视频推理的渐进式训练 显示英文标题

标题： ReasonAct: Progressive Training for Fine-Grained Video Reasoning in Small Models

摘要： 虽然最近的多模态模型在视觉-语言任务中取得了进展，但小规模变体在视频理解所需的细粒度时间推理方面仍然存在困难。 我们引入了ReasonAct，这是一种通过三阶段训练过程增强小型模型视频推理的方法：首先通过仅文本推理建立基础，然后在视频上进行微调，最后通过时间感知强化学习进行精炼。 我们通过将时间一致性建模引入策略优化，改进了时间组相对策略优化（T-GRPO）。 我们还提出了一种生物力学驱动的子动作分解机制，为构成动作阶段提供渐进式奖励。 在HMDB51、UCF-101和Kinetics-400上的实验表明，我们的3B参数模型分别达到了67.2%、94.1%和78.9%的准确率，分别比基线提高了17.9、15.8和12.3分。 消融研究验证了我们的渐进式训练方法使小型模型能够在保持计算效率的同时实现具有竞争力的视频推理性能。 显示英文摘要

摘要： While recent multimodal models have shown progress in vision-language tasks, small-scale variants still struggle with the fine-grained temporal reasoning required for video understanding. We introduce ReasonAct, a method that enhances video reasoning in smaller models through a three-stage training process: first building a foundation with text-only reasoning, then fine-tuning on video, and finally refining with temporal-aware reinforcement learning. We build upon Temporal Group Relative Policy Optimization (T-GRPO) by incorporating temporal consistency modeling into policy optimization. We also propose a biomechanically-motivated sub-action decomposition mechanism that provides graduated rewards for constituent action phases. Through experiments on HMDB51, UCF-101, and Kinetics-400, our 3B-parameter model achieves 67.2%, 94.1%, and 78.9% accuracy respectively, demonstrating improvements of 17.9, 15.8, and 12.3 points over baselines. Ablation studies validate that our progressive training methodology enables smaller models to achieve competitive video reasoning performance while maintaining computational efficiency.