IRPO: Boosting Image Restoration via Post-training GRPO
IRPO applies GRPO post-training to image restoration via selective hard-sample data and multi-component rewards, improving in-domain accuracy by 0.93 dB and OOD generalization by 3.43 dB.
Published 2026Paris Poster Session 2 · Wed, Dec 9, 5:00 PM–7:00 PM local time · Paris Poster HallarXiv ↗OpenReview ↗
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Abstract
Post-training has become effective for high-level generation, but its role in low-level vision remains underexplored. Existing image restoration methods often rely on fixed pixel-wise fitting to ground-truth images, which can lead to over-smoothing and weak generalization. We propose IRPO, a GRPO-based post-training framework for deterministic restoration models. IRPO is built around two axes: data formulation and reward modeling. For data formulation, we select the 30% underperforming samples from the pre-training stage, which improves both accuracy and training efficiency. For reward modeling, we combine fidelity-oriented and quality-aware feedback with three components: a General Reward for structural fidelity, an Expert Reward that uses a Vision-Language Model as a coarse visual-quality judge, and a Restoration Reward for task-specific low-level cues. Experiments on six in-domain and five out-of-domain (OOD) benchmarks show that IRPO improves the AdaIR baseline by 0.93 dB on in-domain tasks and 3.43 dB on OOD settings. Our code can be shown in https://github.com/HaoxuanXU1024/IRPO.