EVA: Efficient Reinforcement Learning for End-to-End Video Agent

📰 ArXiv cs.AI

EVA enables efficient reinforcement learning for end-to-end video agents using multimodal large language models

advanced Published 25 Mar 2026

Action Steps

Utilize multimodal large language models (MLLMs) for video understanding
Apply reinforcement learning to enable adaptive reasoning and efficient processing of video frames
Integrate EVA with existing agent-based methods to improve performance

Who Needs to Know This

AI engineers and researchers working on video understanding and multimodal models can benefit from EVA, as it improves the efficiency of reinforcement learning for end-to-end video agents

Key Insight

💡 EVA improves the efficiency of reinforcement learning for video agents by leveraging multimodal large language models

Key Takeaways

EVA enables efficient reinforcement learning for end-to-end video agents using multimodal large language models

Full Article

Title: EVA: Efficient Reinforcement Learning for End-to-End Video Agent

Abstract:
arXiv:2603.22918v1 Announce Type: cross 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 p

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