Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917340655910912 |
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| author | Cao, Sihan Zhang, Jianwei Zheng, Pengcheng Yan, Jiaxin Qin, Caiyan Ye, Yalan Dong, Wei Wang, Peng Yang, Yang Zhang, Chaoning |
| author_facet | Cao, Sihan Zhang, Jianwei Zheng, Pengcheng Yan, Jiaxin Qin, Caiyan Ye, Yalan Dong, Wei Wang, Peng Yang, Yang Zhang, Chaoning |
| contents | Large Vision-Language Models (LVLMs) incur substantial inference costs due to the processing of a vast number of visual tokens. Existing methods typically struggle to model progressive visual token reduction as a multi-step decision process with sequential dependencies and often rely on hand-engineered scoring rules that lack adaptive optimization for complex reasoning trajectories. To overcome these limitations, we propose TPRL, a reinforcement learning framework that learns adaptive pruning trajectories through language-guided sequential optimization tied directly to end-task performance. We formulate visual token pruning as a sequential decision process with explicit state transitions and employ a self-supervised autoencoder to compress visual tokens into a compact state representation for efficient policy learning. The pruning policy is initialized through learning from demonstrations and subsequently fine-tuned using Proximal Policy Optimization (PPO) to jointly optimize task accuracy and computational efficiency. Our experimental results demonstrate that TPRL removes up to 66.7\% of visual tokens and achieves up to a 54.2\% reduction in FLOPs during inference while maintaining a near-lossless average accuracy drop of only 0.7\%. Code is released at \href{https://github.com/MagicVicCoder/TPRL}{\textcolor{mypink}{https://github.com/MagicVicCoder/TPRL}}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13394 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models Cao, Sihan Zhang, Jianwei Zheng, Pengcheng Yan, Jiaxin Qin, Caiyan Ye, Yalan Dong, Wei Wang, Peng Yang, Yang Zhang, Chaoning Computer Vision and Pattern Recognition Large Vision-Language Models (LVLMs) incur substantial inference costs due to the processing of a vast number of visual tokens. Existing methods typically struggle to model progressive visual token reduction as a multi-step decision process with sequential dependencies and often rely on hand-engineered scoring rules that lack adaptive optimization for complex reasoning trajectories. To overcome these limitations, we propose TPRL, a reinforcement learning framework that learns adaptive pruning trajectories through language-guided sequential optimization tied directly to end-task performance. We formulate visual token pruning as a sequential decision process with explicit state transitions and employ a self-supervised autoencoder to compress visual tokens into a compact state representation for efficient policy learning. The pruning policy is initialized through learning from demonstrations and subsequently fine-tuned using Proximal Policy Optimization (PPO) to jointly optimize task accuracy and computational efficiency. Our experimental results demonstrate that TPRL removes up to 66.7\% of visual tokens and achieves up to a 54.2\% reduction in FLOPs during inference while maintaining a near-lossless average accuracy drop of only 0.7\%. Code is released at \href{https://github.com/MagicVicCoder/TPRL}{\textcolor{mypink}{https://github.com/MagicVicCoder/TPRL}}. |
| title | Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.13394 |