OARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution

Aligning generative real-world image super-resolution mod-els with human visual preference is challenging due to the perception–fidelity trade-off and diverse, unknown degradations. Prior approachesrely on offline preference optimization and static metric aggregation,which are often non-interpretable and prone to pseudo-diversity understrong conditioning. We propose OARS, a process-aware online align-ment framework built on COMPASS, a MLLM-based reward that eval-uates the LR→SR transition by jointly modeling fidelity preservation andperceptual gain with an input-quality-adaptive trade-off. To train COM-PASS, we curate COMPASS-20K spanning synthetic and real degrada-tions, and introduce a three-stage perceptual annotation pipeline thatyields calibrated, fine-grained training labels. Guided by COMPASS,OARS performs progressive online alignment from cold-start flow match-ing to full-reference and finally reference-free RL via shallow LoRA opti-mization for on-policy exploration. Extensive experiments and user stud-ies demonstrate consistent perceptual improvements while maintainingfidelity, achieving state-of-the-art performance on Real-ISR benchmarks.