Learning Active Perception for Pixel-Space Reasoning via Visual-Intent Stratified GRPO

Pixel-space reasoning enables Vision-Language Models to perform active perception: instead of answering from a single global view, the model can zoom into high-resolution regions to gather finegrained evidence. This mirrors human visual problem solving, which involves broad exploration (finding relevant cues) and local verification (confirming details). However, most pixel-space reasoning systems are trained with Group Relative Policy Optimization (GRPO), whose groupwise advantage normalization directly compares sampled rollouts that follow different perception strategies. Rollouts with different zoom depths and different visual intents (explore vs. verify) are normalized together, making exploratory multi-zoom behavior appear disadvantageous: the multi-zoom strategies newly discovered during training typically yield lower mean rewards than already-mastered single-zoom or no-zoom strategies. This leads to visual laziness: the policy does not explore active multi-zoom perception strategies and collapses into a zoom-averse state. To address this, we propose Visual-Intent Stratified GRPO (VIS-GRPO), a drop-in replacement for GRPO that restores fair learning signals for active perception. VIS-GRPO computes advantages only among strategically comparable rollouts by stratifying trajectories along (i) zoom depth and (ii) visual intent, separating broad search over diverse regions from local verification over overlapping regions. This alignment prevents exploratory trajectories from being overshadowed and encourages the learning of active perception. Extensive experiments demonstrate that VIS-GRPO enables effective multi-zoom active perception strategies and consistently improves performance across challenging visual understanding benchmarks like HR-Bench and MME-RealWorld.