@inproceedings{kan-etal-2026-rubricrm,
title = "{R}ubric{RM}: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing",
author = "Kan, Zijian and
Wang, Wei and
Long, Luo and
Zhao, Bing and
Ren, Xuan and
Qiao, Weixu and
Wenbo, Li and
Wei, hu and
Qu, Lin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1094/",
pages = "25107--25126",
ISBN = "979-8-89176-459-0",
abstract = "Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot adapt to different instructions. This limits both interpretability and task sensitivity, especially for text-to-image generation and instruction-based image editing, where different inputs require different evaluation dimensions. We propose RubricRM, a pairwise generative reward modeling framework that first produces an input-specific rubric with evaluation dimensions, weights, and scoring criteria, and then applies the rubric to score candidate images. We train dedicated RubricRM models for text-to-image generation and image editing using a two-stage training pipeline: supervised fine-tuning teaches the model the rubric-based scoring paradigm, while GRPO further improves scoring through fine-grained dimension-level rewards. Experiments on multiple generation and editing benchmarks show that RubricRM outperforms existing specialized reward models and remains competitive with strong proprietary MLLM judges despite using smaller backbones. Our models, data, and code are available at https://github.com/zijiankan/RubricRM."
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<abstract>Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot adapt to different instructions. This limits both interpretability and task sensitivity, especially for text-to-image generation and instruction-based image editing, where different inputs require different evaluation dimensions. We propose RubricRM, a pairwise generative reward modeling framework that first produces an input-specific rubric with evaluation dimensions, weights, and scoring criteria, and then applies the rubric to score candidate images. We train dedicated RubricRM models for text-to-image generation and image editing using a two-stage training pipeline: supervised fine-tuning teaches the model the rubric-based scoring paradigm, while GRPO further improves scoring through fine-grained dimension-level rewards. Experiments on multiple generation and editing benchmarks show that RubricRM outperforms existing specialized reward models and remains competitive with strong proprietary MLLM judges despite using smaller backbones. Our models, data, and code are available at https://github.com/zijiankan/RubricRM.</abstract>
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%0 Conference Proceedings
%T RubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing
%A Kan, Zijian
%A Wang, Wei
%A Long, Luo
%A Zhao, Bing
%A Ren, Xuan
%A Qiao, Weixu
%A Wenbo, Li
%A Wei, hu
%A Qu, Lin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kan-etal-2026-rubricrm
%X Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot adapt to different instructions. This limits both interpretability and task sensitivity, especially for text-to-image generation and instruction-based image editing, where different inputs require different evaluation dimensions. We propose RubricRM, a pairwise generative reward modeling framework that first produces an input-specific rubric with evaluation dimensions, weights, and scoring criteria, and then applies the rubric to score candidate images. We train dedicated RubricRM models for text-to-image generation and image editing using a two-stage training pipeline: supervised fine-tuning teaches the model the rubric-based scoring paradigm, while GRPO further improves scoring through fine-grained dimension-level rewards. Experiments on multiple generation and editing benchmarks show that RubricRM outperforms existing specialized reward models and remains competitive with strong proprietary MLLM judges despite using smaller backbones. Our models, data, and code are available at https://github.com/zijiankan/RubricRM.
%U https://aclanthology.org/2026.emnlp-main.1094/
%P 25107-25126
Markdown (Informal)
[RubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing](https://aclanthology.org/2026.emnlp-main.1094/) (Kan et al., EMNLP 2026)
ACL
- Zijian Kan, Wei Wang, Luo Long, Bing Zhao, Xuan Ren, Weixu Qiao, Li Wenbo, hu Wei, and Lin Qu. 2026. RubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 25107–25126, Budapest, Hungary. Association for Computational Linguistics.