Abstract
Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning depends on the correct ordering and evolution of events. This deficiency stems in part from training objectives that fail to explicitly reward temporal reasoning and instead rely on frame-level spatial shortcuts. To address this limitation, we propose Temporal Global Policy Optimization (TGPO), a reinforcement learning with verifiable rewards (RLVR) algorithm designed to incentivize temporal awareness in MLLMs. TGPO contrasts model outputs generated from temporally ordered versus shuffled video frames to derive calibrated, globally normalized reward signals that explicitly favor temporally coherent reasoning. Integrated with GRPO and GSPO, TGPO supports cold-start RL training and effectively suppresses spatial shortcut behaviors learned by existing MLLMs. Experiments across five egocentric video benchmarks demonstrate that TGPO consistently improves temporal grounding and causal coherence, outperforming prior RL-based video reasoning approaches. Our results suggest that TGPO offers a simple and scalable pathway toward temporally robust MLLMs for egocentric video understanding.
Abstract (translated)
多模态大语言模型（MLLMs）近期在视觉理解方面展现出强劲性能，但往往缺乏时序感知能力，尤其在依赖事件正确排序与演变的第一人称视角场景中。这一缺陷部分源于训练目标未能明确奖励时序推理，而仅依赖帧级空间捷径。为应对此局限，我们提出时序全局策略优化（TGPO）——一种基于可验证奖励的强化学习（RLVR）算法，旨在激发MLLMs的时序感知能力。TGPO通过对比模型对时序有序与打乱视频帧的输出，生成经校准的全局归一化奖励信号，明确优先选择时序连贯的推理。TGPO与GRPO及GSPO相结合，支持冷启动强化学习训练，并有效抑制现有MLLMs习得的空间捷径行为。在五个第一人称视频基准测试上的实验表明，TGPO持续提升时序定位与因果连贯性，优于此前基于强化学习的视频推理方法。研究结果表明，TGPO为构建面向第一人称视频理解的时序鲁棒性MLLMs提供了简单且可扩展的路径。
URL
https://arxiv.org/abs/2603.27184
