VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models

Visual token pruning methods effectively mitigate the quadratic computational growth caused by processing high-resolution images or long video frames in vision-language models (VLMs). However, existing approaches rely on predefined pruning configurations without determining whether they achieve computationperformance optimality. In this work, we introduce VisPCO, a novel framework that formulates visual token pruning as a Pareto configuration optimization problem to automatically identify optimal configurations. Our approach employs continuous relaxation and straightthrough estimators to enable gradient-based search, solved via the Augmented Lagrangian method. Extensive experiments across 8 visual benchmarks demonstrate that VisPCO effectively approximates the empirical Pareto frontier obtained through grid search and generalizes well across various pruning methods and VLM architectures. Furthermore, through learnable kernel functions, we investigate layerwise pruning patterns and reveal that multi-step progressive pruning captures VLMs' hierarchical compression structure, achieving superior computation-performance trade-offs compared to single-layer approaches.