EditReward: A Human-Aligned Reward Model for Instruction-Guided Image
  Editing
Recently, we have witnessed great progress in image editing with natural
language instructions. Several closed-source models like GPT-Image-1, Seedream,
and Google-Nano-Banana have shown highly promising progress. However, the
open-source models are still lagging. The main bottleneck is the lack of a
reliable reward model to scale up high-quality synthetic training data. To
address this critical bottleneck, we built \mname, trained with our new
large-scale human preference dataset, meticulously annotated by trained experts
following a rigorous protocol containing over 200K preference pairs. \mname
demonstrates superior alignment with human preferences in instruction-guided
image editing tasks. Experiments show that \mname achieves state-of-the-art
human correlation on established benchmarks such as GenAI-Bench, AURORA-Bench,
ImagenHub, and our new \benchname, outperforming a wide range of VLM-as-judge
models. Furthermore, we use \mname to select a high-quality subset from the
existing noisy ShareGPT-4o-Image dataset. We train Step1X-Edit on the selected
subset, which shows significant improvement over training on the full set. This
demonstrates \mname's ability to serve as a reward model to scale up
high-quality training data for image editing. Furthermore, its strong alignment
suggests potential for advanced applications like reinforcement learning-based
post-training and test-time scaling of image editing models. \mname with its
training dataset will be released to help the community build more high-quality
image editing training datasets.
68de90a170ada21878c750c2
Keming Wu
68de90a170ada21878c750c3
Sicong Jiang
claimed_verified
2025-10-03T12:38:24.555Z
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Sicong Jiang
user
Jo1nsonJiang
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Max Ku
claimed_verified
2025-10-15T07:08:15.514Z
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Max Ku
user
vinesmsuic
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Ping Nie
claimed_verified
2025-10-03T12:38:21.658Z
65358802a920f38780b3248a
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Ping Nie
user
pingnieuk
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Minghao Liu
claimed_verified
2025-10-24T16:15:03.522Z
6417d9ea8f689506e7148417
https://cdn-avatars.huggingface.co/v1/production/uploads/6417d9ea8f689506e7148417/bAYcruWNw4WvmuQcGgcwC.jpeg
minghao
user
Liam-Liu
68de90a170ada21878c750c7
Wenhu Chen
EditReward: A Human-Aligned Reward Model for Instruction-Guided Image
  Editing
Recently, we have witnessed great progress in image editing with natural
language instructions. Several closed-source models like GPT-Image-1, Seedream,
and Google-Nano-Banana have shown highly promising progress. However, the
open-source models are still lagging. The main bottleneck is the lack of a
reliable reward model to scale up high-quality synthetic training data. To
address this critical bottleneck, we built \mname, trained with our new
large-scale human preference dataset, meticulously annotated by trained experts
following a rigorous protocol containing over 200K preference pairs. \mname
demonstrates superior alignment with human preferences in instruction-guided
image editing tasks. Experiments show that \mname achieves state-of-the-art
human correlation on established benchmarks such as GenAI-Bench, AURORA-Bench,
ImagenHub, and our new \benchname, outperforming a wide range of VLM-as-judge
models. Furthermore, we use \mname to select a high-quality subset from the
existing noisy ShareGPT-4o-Image dataset. We train Step1X-Edit on the selected
subset, which shows significant improvement over training on the full set. This
demonstrates \mname's ability to serve as a reward model to scale up
high-quality training data for image editing. Furthermore, its strong alignment
suggests potential for advanced applications like reinforcement learning-based
post-training and test-time scaling of image editing models. \mname with its
training dataset will be released to help the community build more high-quality
image editing training datasets.
A new reward model, trained with a large-scale human preference dataset, improves instruction-guided image editing by selecting high-quality training data and achieving state-of-the-art performance on benchmarks.
reward model human preference dataset instruction-guided image editing GenAI-Bench AURORA-Bench ImagenHub reinforcement learning post-training test-time scaling