EVA: Efficient Reinforcement Learning for End-to-End Video Agent
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-reflecti...
| Authors | Yaolun Zhang, Ruohui Wang, Jiahao Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 March 2026 |
| Last updated at source | 26 March 2026 |
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