[2602.01649] Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.01649 (cs) [Submitted on 2 Feb 2026 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning Authors:Yinchao Ma, Qiang Zhou, Zhibin Wang, Xianing Chen, Hanqing Yang, Jun Song, Bo Zheng View a PDF of the paper titled Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning, by Yinchao Ma and 6 other authors View PDF HTML (experimental) Abstract:Video large language models have demonstrated remarkable capabilities in video understanding tasks. However, the redundancy of video tokens introduces significant computational overhead during inference, limiting their practical deployment. Many compression algorithms are proposed to prioritize retaining features with the highest attention scores to minimize perturbations in attention computations. However, the correlation between attention scores and their actual contribution to correct answers remains ambiguous. To address the above limitation, we propose a novel \textbf{C}ontribution-\textbf{a}ware token \textbf{Co}mpression algorithm for \textbf{VID}eo understanding (\textbf{CaCoVID}) that explicitly optimizes the token selection policy based on the contribution of tokens to correct predictions. First, we introduce a reinforcement learning-based framework that optimizes a policy network to select video token combinations wit...