Dynamic Token Compression for Efficient Video Understanding through Reinforcement Learning
Multimodal Large Language Models have demonstrated remarkable capabilities in video understanding, yet face prohibitive computational costs and performance degradation from ''context rot'' due to massive visual token redundancy. Existing compression strategies typically rely on heuristics or fixed transformations that are often decoupled from the downstream task objectives, limiting their adaptability and effectiveness. To address this, we propose SCORE (Surprise-augmented token COmpression via REinforcement learning), a unified framework that learns an adaptive token compression policy. SCORE introduces a lightweight policy network conditioned on a surprise-augmented state representation...
| Authors | Shida Wang, YongXiang Hua, Zhou Tao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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