EventLens：用事件结构强化学习改进视频时序理解
EventLens: Event-Structure Reinforcement Learning for Video Understanding
给视频模型换个练法：不背字幕改练事件排序，GRPO 免人工标注，Qwen3-VL 上实测有效。
论文把多模态大模型的时序短板归纳为三类错误：合并相邻事件、漏看单事件进程、推断不合理的跨事件关系，并将其归因于字幕式监督让模型依赖语言捷径。EventLens 框架不再重建字幕，而是让模型在受控扰动下恢复时序结构，扰动包括改变时间粒度、倒放进程、打乱事件顺序三种。方法基于三级时序本体设计出事件分割、进程判别、多事件关系推理三类可验证任务，奖励是确定性的，因此可用 GRPO 做任务专项训练，无需人类偏好标注或奖励模型。为合并多个专项策略，作者提出多教师在线蒸馏 MT-OPD，并在 Qwen3-VL 主干上实验，时序定位与事件敏感推理基准成绩提升，通用视频问答性能保持不变。学习曲线显示，与直接混合 RL 优化相比，EventLens 达到相近的下游迁移效果且训练成本明显更低。
EventLens: Event-Structure Reinforcement Learning for Video Understanding
Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure that makes a video coherent. They may merge neighboring events, miss single-event progression, or infer implausible relations across events. We study structured temporal understanding as the ability to recover event structure from visual evidence, including event boundaries, single-event progression, and multi-event relations. We argue that this gap arises from a mismatch between current post-training objectives and temporal reasoning, where caption-style supervision allows models to rely on language shortcuts rather than visual temporal grounding. To address this, we propose EventLens, a vision-centric reinforcement learning framework that learns video understanding through event-structure recovery. Instead of reconstructing captions, EventLens trains models to recover temporal structure under controlled perturbations, such as altered temporal granularity, reversed progression, and shuffled event order. We instantiate this framework with a three-level temporal ontology and derive three verifiable task families: event segmentation, progression discrimination, and multi-event relation reasoning. These tasks admit deterministic rewards, enabling task-specialized GRPO training without human preference labels or learned reward models. We further introduce multi-teacher on-policy distillation (MT-OPD) to consolidate specialized policies into a unified model. Experiments on Qwen3-VL backbones show consistent improvements on temporal grounding and event-sensitive reasoning benchmarks, while preserving general video-QA performance. Learning curves further demonstrate that EventLens achieves comparable downstream transfer with significantly reduced training cost compared to direct mixed RL optimization.