Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866918384524853248 |
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| author | Zhao, Xiangyu Zhang, Peiyuan Lin, Junming Liang, Tianhao Duan, Yuchen Ding, Shengyuan Tian, Changyao Zang, Yuhang Yan, Junchi Yang, Xue |
| author_facet | Zhao, Xiangyu Zhang, Peiyuan Lin, Junming Liang, Tianhao Duan, Yuchen Ding, Shengyuan Tian, Changyao Zang, Yuhang Yan, Junchi Yang, Xue |
| contents | 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 primarily assessed via instruction following. Using these pipelines, we collect the FIRM-Edit-370K and FIRM-Gen-293K datasets, and train specialized reward models (FIRM-Edit-8B and FIRM-Gen-8B) that accurately reflect these criteria. Second, we introduce FIRM-Bench, a comprehensive benchmark specifically designed for editing and generation critics. Evaluations demonstrate that our models achieve superior alignment with human judgment compared to existing metrics. Furthermore, to seamlessly integrate these critics into the RL pipeline, we formulate a novel "Base-and-Bonus" reward strategy that balances competing objectives: Consistency-Modulated Execution (CME) for editing and Quality-Modulated Alignment (QMA) for generation. Empowered by this framework, our resulting models FIRM-Qwen-Edit and FIRM-SD3.5 achieve substantial performance breakthroughs. Comprehensive experiments demonstrate that FIRM mitigates hallucinations, establishing a new standard for fidelity and instruction adherence over existing general models. All of our datasets, models, and code have been publicly available at https://firm-reward.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12247 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation Zhao, Xiangyu Zhang, Peiyuan Lin, Junming Liang, Tianhao Duan, Yuchen Ding, Shengyuan Tian, Changyao Zang, Yuhang Yan, Junchi Yang, Xue Computer Vision and Pattern Recognition 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 primarily assessed via instruction following. Using these pipelines, we collect the FIRM-Edit-370K and FIRM-Gen-293K datasets, and train specialized reward models (FIRM-Edit-8B and FIRM-Gen-8B) that accurately reflect these criteria. Second, we introduce FIRM-Bench, a comprehensive benchmark specifically designed for editing and generation critics. Evaluations demonstrate that our models achieve superior alignment with human judgment compared to existing metrics. Furthermore, to seamlessly integrate these critics into the RL pipeline, we formulate a novel "Base-and-Bonus" reward strategy that balances competing objectives: Consistency-Modulated Execution (CME) for editing and Quality-Modulated Alignment (QMA) for generation. Empowered by this framework, our resulting models FIRM-Qwen-Edit and FIRM-SD3.5 achieve substantial performance breakthroughs. Comprehensive experiments demonstrate that FIRM mitigates hallucinations, establishing a new standard for fidelity and instruction adherence over existing general models. All of our datasets, models, and code have been publicly available at https://firm-reward.github.io. |
| title | Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.12247 |