VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

Guangchuan Lv; Dianxing Shi; Dingjie FU

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arXiv · 2609.24485

VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

Guangchuan Lv·Dianxing Shi·Dingjie FU

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Abstract

Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction often causes substantial performance degradation. We identify three key factors behind this degradation: text-guided selection bias, information loss from discarded tokens, and positional distortion caused by sequence compaction. Based on these observations, we propose \textbf{VPRune}, a training-free pre-LLM pruning framework consisting of visual-only diversity selection, similarity-guided token recycling, and position-preserving restoration. Experiments on FastVLM-1.5B across multiple vision-language benchmarks demonstrate that VPRune achieves a favorable accuracy--compression trade-off, with particularly pronounced advantages under aggressive compression. Furthermore, evaluations on edge-device show that VPRune effectively reduces end-to-end inference latency while maintaining superior task performance, demonstrating its practicality for resource-constrained LVLM deployment.

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BibTeX RIS

Guangchuan Lv, Dianxing Shi, Dingjie FU. 2026-09-21. VPRune: Efficient Training-free Pre-LLM Visual Token Pruning. https://arxiv.org/abs/2609.24485

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