Reinforcing Vision-Language Models for Image Quality Assessment with Grounding Process Rewards

Image Quality Assessment (IQA) requires aligning modeloutputs with human perception. While Vision-Language Models (VLMs)show promise, current methods struggle with generalization due to alack of fine-grained reasoning alignment. Existing RL-based approachesprimarily rely on outcome rewards; however, their inherent sparsity oftenresults in accurate predictions based on erroneous logical attributions.This phenomenon creates a critical bottleneck that stifles the develop-ment of robust reasoning capabilities and limits performance gains inout-of-distribution scenarios. To address this problem, we propose RGPR,which utilizes grounding process rewards to orchestrate VLM trajectoriesalong a human-emulated reasoning chain. To maintain optimization sta-bility, we introduce step-wise reward normalization to preserve trainingsignal precision. RGPR achieves state-of-the-art results across three tasks,demonstrating superior reasoning fidelity and robust generalization.