Abstract
Video understanding with multimodal large language models (MLLMs) remains challenging due to the long token sequences of videos, which contain extensive temporal dependencies and redundant frames. Existing approaches typically treat MLLMs as passive recognizers, processing entire videos or uniformly sampled frames without adaptive reasoning. Recent agent-based methods introduce external tools, yet still depend on manually designed workflows and perception-first strategies, resulting in inefficiency on long videos. We present EVA, an Efficient Reinforcement Learning framework for End-to-End Video Agent, which enables planning-before-perception through iterative summary-plan-action-reflection reasoning. EVA autonomously decides what to watch, when to watch, and how to watch, achieving query-driven and efficient video understanding. To train such agents, we design a simple yet effective three-stage learning pipeline - comprising supervised fine-tuning (SFT), Kahneman-Tversky Optimization (KTO), and Generalized Reward Policy Optimization (GRPO) - that bridges supervised imitation and reinforcement learning. We further construct high-quality datasets for each stage, supporting stable and reproducible training. We evaluate EVA on six video understanding benchmarks, demonstrating its comprehensive capabilities. Compared with existing baselines, EVA achieves a substantial improvement of 6-12% over general MLLM baselines and a further 1-3% gain over prior adaptive agent methods. Our code and model are available at this https URL.
Abstract (translated)
基于多模态大语言模型（MLLMs）的视频理解仍具挑战性，因为视频包含长序列令牌，具有大量时间依赖性和冗余帧。现有方法通常将MLLMs视为被动识别器，处理整个视频或均匀采样的帧，缺乏自适应推理。近期基于智能体的方法引入外部工具，但仍依赖手动设计的工作流和感知优先策略，导致长视频处理效率低下。我们提出EVA——一个用于端到端视频智能体的高效强化学习框架，通过迭代总结-规划-行动-反思的推理机制实现“先规划后感知”。EVA能自主决定观看内容、时机与方式，实现查询驱动的高效视频理解。为训练此类智能体，我们设计了简单有效的三阶段学习流程——包括监督微调（SFT）、卡尼曼-特沃斯基优化（KTO）和广义奖励策略优化（GRPO）——从而桥接监督模仿与强化学习。我们还为各阶段构建高质量数据集，支持稳定可复现的训练。我们在六个视频理解基准测试中评估EVA，展示其综合能力。相比现有基线，EVA较通用MLLM基线提升6-12%，并较此前自适应智能体方法额外提升1-3%。代码与模型已公开。
URL
https://arxiv.org/abs/2603.22918
