Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation
Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and text-to-image (T2I) generation. However, current reward models, which act as critics during RL, often suffer from hallucinations and assign noisy scores, inherently misguiding the optimization process. In this paper, we present FIRM (Faithful Image Reward Modeling), a comprehensive framework that develops robust reward models to provide accurate and reliable guidance for faithful image generation and editing. First, we design tailored data curation pipelines to construct high-quality scoring datasets. Specifically, we evaluate editing using both execution and consistency, while generation is pr...
| Authors | Xiangyu Zhao, Peiyuan Zhang, Junming Lin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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