GRIP-VLM: Group-Relative Importance Pruning for Efficient Vision-Language Models

• URL: http://arxiv.org/abs/2605.13375v1
• Date: Wed, 13 May 2026 11:32:03 GMT
• Title: GRIP-VLM: Group-Relative Importance Pruning for Efficient Vision-Language Models
• Abstract summary: In Vision-Language Models (VLMs) processing a massive number of visual tokens incurs prohibitive computational retraining.<n>GRIP-VLM employs a Group Relative Policy (GRPO) paradigm anchored by supervised pruning to directly explore space selection.
• Score: 19.21570577065149
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In Vision-Language Models (VLMs), processing a massive number of visual tokens incurs prohibitive computational overhead. While recent training-aware pruning methods attempt to selectively discard redundant tokens, they largely rely on continuous-gradient relaxations. However, visual token pruning is inherently a discrete, non-convex combinatorial problem; consequently, these continuous approximations frequently trap the optimization in sub-optimal local minima, especially under aggressive compression budgets. To overcome this fundamental bottleneck, we propose GRIP-VLM, a Group-Relative Importance Pruning framework driven by Reinforcement Learning. Rather than relying on smooth-gradient assumptions, GRIP-VLM formulates pruning as a Markov Decision Process, employing a Group Relative Policy Optimization (GRPO) paradigm anchored by supervised warm-up to directly explore the discrete selection space. Integrated with a budget-aware scorer, our lightweight agent dynamically evaluates per-token importance and adapts to arbitrary compression ratios without retraining. Extensive experiments across diverse multimodal benchmarks demonstrate that GRIP-VLM consistently outperforms heuristic and supervised-learning baselines, achieving a superior Pareto frontier and delivering up to a 15\% inference speedup at equal accuracy.

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