HART: High-Resolution Annotation-Free Reasoning Technique through a Closed-loop Framework

Current Large Multimodal Models (LMMs) struggle withhigh-resolution visual inputs during the reasoning process, as the num-ber of image tokens increases quadratically with resolution, introducingsubstantial redundancy and irrelevant information. A common practiceis to identify key image regions and refer to their high-resolution counter-parts during reasoning, typically trained with external visual supervision.However, such visual supervision cues require costly grounding labelsfrom human annotators. Meanwhile, it remains an open question how toenhance a model’s grounding abilities to support reasoning without rely-ing on additional annotations. In this paper, we propose High-resolutionAnnotation-free Reasoning Technique (HART), a closed-loop frameworkthat enables LMMs to focus on and self-verify key regions of high-resolution visual inputs. HART incorporates a post-training paradigmin which we design Advantage Preference Group Relative Policy Opti-mization (AP-GRPO) to encourage accurate localization of key regionswithout external visual annotations. Notably, HART provides explain-able reasoning pathways and enables efficient optimization of localiza-tion. Extensive experiments on MME-RealWorld-Lite, TreeBench, V*Bench, HR-Bench-4K/8K, and MMStar demonstrate that HART im-proves performance across a wide range of high-resolution visual tasks,consistently outperforming strong baselines. Code will be available athttps://github.com/RL-MIND/HART.