EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing
Authors: Keming Wu
Sicong Jiang
Max Ku
Ping Nie
Minghao Liu
Wenhu Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Image Editing
Reward Model
Generative Model Evaluation
Primary area: datasets and benchmarks
TL;DR: We present EDITREWARD, trained on EDITREWARD-DATA (200K human preference pairs), and introduce EDITREWARD-BENCH, setting new state-of-the-art in image editing evaluation and data curation.

Abstract: 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 EditReward, trained with our new large-scale human preference dataset, meticulously annotated by trained experts following a rigorous protocol containing over 200K preference pairs. EditReward demonstrates superior alignment with human preferences in instruction-guided image editing tasks.
Experiments show that EditReward achieves state-of-the-art human correlation on established benchmarks such as GenAI-Bench, AURORA-Bench, ImagenHub, and our new EditReward-Bench, outperforming a wide range of VLM-as-judge models. Furthermore, we use EditReward 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 EditReward's ability to serve as a reward model to scale up high-quality training data for image editing. EditReward with its training dataset will be released to help the community build more high-quality image editing training datasets to catch up with the frontier ones.

Published as a conference paper at ICLR 2026
EDITREWARD: A HUMAN-ALIGNER REWARD MODEL FOR INSTRUCTION-GUIDED IMAGE EDITING
Keminng W Jiang Max Ku', PPing Nie Minghao Liu?, WWnnh Chen University of Waterloo, 'Tsinghua Univerisity, 32077AI, "McGill University, 'Independent [wukeming0608e @gmal0 com, wenhucherr@waterrloo. ca}
ABSTRACT
Recently, we have witnessed great progress in image editing with natural lan- guage instructions. Several closed-source models like GPT-Image-I, Seedream, and Google-Nano-Bane Mano-Banana have shown highly promising progress. However, the open-source models are still lagging. The main bottleneck the lack of reli- able reward model to scale up high-quality synthetic training data. To address this critical bottleneck, built EDITREWARD, trained with our new large-scale human preference dataset, meticulously annotated by trained experts following a rigorous protocol containing over 200K preference pairs. EDITREWARD demon- strates superior alignment with human preferences in instruction guided image editing tasks. Experiments show that EDITREWARD achieves state-of-the-an hu- man correlation on established benchmarks such GenAl-Bench, AURORA- Bench, ImagenHub, and our new EDITREWARD- BENCH, outperforming a wide range of VLM-as-judge models. Furthermore, use EDITREWARY to select high-quality subset from the existing noisy ShareGPT-4o-Imag dataset. We train StepIX- X-Eii on the selected subset, which shows significant improvement over training on the full set. This demonstrates EDITREWARD' 's ability to serve as a reward model to scale up high-quality training data for image editing. EDITRE- WARD with its training dataset will be released to help the community build more high-quality image editing training datasets to catch up with the frontier ones.
INTRODUCTION
Instruction-guiin image editing is an important task to enable intuitive and fine-grained image mod- ifications through natural language instructions (Brooks al. 2023 Zhang et al. 2024a Zhao al.,
2024 Wei et al., (2024). Closed-source models like GPT-Immae- Image- 1 OpenAA| 2025), Seedream (Gao al. 2025). and Google's Nano Banana (Google DeepMind] (025) have made marvelous strides on this task. The progress is driven partially by their high-quality in-house private training dataset, Ex- isting open-source image editing datasets like ImgEdit Yee et al. 2025), HQ-Edit (Hui et al.) 2024). GPT-Image-EEit- 1.5 (Wang et al. 2025d), UltraEdit (Zhao et al. 22024), and OmniEdit (Wei et al. 2024) are all produced with automatic data synthesis pipelines and filtered with different rewards.
Commonly used rewards are mainly divided into three categories: (1) Perceptual scores like LPIPS (Zhang et al. 2018) fail to capture semantic alignment with user instructions, (2) Feature al. 2021) fail to capture editing semanties, (3) VLM-as-a-judge like VIEScore (Ku et al., 2023) Jiang et al. 2024 Wang et al. (025b) uses general-purpose Vision- Language Models (VLM), which are not optimized for rewarding image editing tasks. Therefore, these ad-hoc rewards show weak alignment with human preference in the image editing task. To build more aligned rewards, line of work proposes fine-tune general-purpose VLMs (for in- stance Qwen2.5-VLL to reward models. However, some of them rely on noisy, crowd-sourced pref- erence annotations (Lin et al. 22024) Xu et al. (024), which are often plagued by inconsistency, low inter-annotator agreement. The others adopt pseudo-labels generated by proprietary. closed-soure models (Wei et al. 2024 Wu et al., 2025c), creating highly noisy and biased labels. These trained
scores like CLIP (Hessel
"These authors contributed equally.

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Published as a conference paper at ICLR 2026
reward models still fall short in providing enough reward signals to scale up high-quality image editing datasets. A high quality image editing dataset is desired to build a good reward model.
In this paper, introduce EDITREWARD, human-aligned reward model powered by high- quality dataset for catructiio-guideen image editing. We first construct EDITREWARD-DATE large-scale, high-fidelity preference dataset for instruction-guider image editing. It comprises over 200K manually annotated preference pairs, covering a diverse range of edits produced by seven state- of-the-art models across twelve distinct sources. Every preference annotation in EDITREWARD- DATA was curated by trained annotators following a rigorous and standardized protocol, ensuring high alignment with considered human judgment and minimizing label noise. Using this dataset, we train the reward model EDITREWARD to score instruction guided image edits. To rigorously assess a new benchmark built
a
EDITREWARD and future models, we also introduce EDITREWARD--BE upon our high-qualit annotations, which includes more difficult multi-way preference prediction.
Experimental results show that EDITREWARD achieves state-of-the-art performance on several benchmarks. On GenAl-Bench (Jiang et al. (024), our model obtains score of 65.72, signifi- cantly outperforming other leading VLM judges such as GPT-5 (59.61). Similarly, on AURORA- Bench (Krojer et al. 2024), EDITREWARD scores 63.62, showing a substantial gain over OpenAI- GPT-4o (50.81). While demonstrating competitive performance on ImagenHub (Ku et al.) 2024) with score of 35,20, it is our proposed EDITREWARD- WARD-BENRHH BENCH where the fine-grained capabil- ities of top models are most clearly discerned. This not only validates the superiority of our model but also demonstrates that BOOTREWARD-BENCEE ation. We further study the potential of EDITREWARD to select the high-quality subset from noisy candidates, which can be used to train next-generation image editing models. Specifically, we adopt EDITREWARD to select the top 20K subset from ShareGPT-4o-Imaae (Chen et al. (025a) and use the subset to fine-tune Step X-Eiit (Liu et al. 2025b). We observe significant improvement by training on the subset over training on the full set. On GEdit-Bench, the overall score increases from 6.7/10 (full-set) to 7.1/10 (subset), making it on par with Doubao-Fdit (Wang et al., (025c). This experiment demonstrates its high potential to work as a reward model for future research.
provides more reliable and challenging evalu-
In summary, our primary contributions are: (1) We construct and release EDITREWARD- DATA, a large-scale (200K) preference dataset for image editing, distinguished by its high-quality manual annotations and diversity of sources. (2) We train and release EDITREWARD, a VLM-based reward model trained EDITREWARD-DAA
that demonstrates superior alignment with human prefer- ences. (3) We propose EDITREWARD BENCH, a new benchmark featuring a more challenging multi-way preference ranking task that provides more robust evaluation of reward models.
EDITREWARD-DATA
2.1 THE EDITREWARD -DATA CONSTRUCTION
EDITREWARD- DATA contains 9557 instruction-image pairs collected from six established editing benchmarks: GEdit-Bench (606) (Liu et al. (025b), ImgEdit EnngdditBBench (737) (Ye et al., (025). Mag- icBrush (1,053) Zhang et al. 2024a), AnyEdit (1,250) (Yu et al. 2025). EmuEdit (5,611) (Sheynin et al. 2024), and an internal set (300). This aggregation ensures broad coverage of semantically grounded and executable editing instructions. For each instruction,
generated 12 candidate im- ages using six state-of-the-an models: Step1 X-Edit (Liu et al. 2025b), KonteKo (BlackForest- Labs al. 2025). Qwen-Image-Ead (Wu et al. (025a), BAGEL (Deng et al. 2025). Ovis-U1 (Wang et al. 2025a), and 0mniGen2 (Wu et al. 2025b), with multiple random seeds to avoid model bias. Seven candidates were randomly sampled for human evaluation. Annotators scored each image on a 4-point Likert scale (1 = Poor and 4 = Excellent) along two dimensions: Instruction Following (semantic accuracy, completeness, and no unprompted changes) and Visual Quality (plausibility, artifact-free rendering, and aesthetics). This rubric yields more informative labels than single-score schemes. Details of the annotation protocol and quality- A.2 Comprehensive statistics of the dataset are provided in Table I and Figure 2 EDITREWARD DATA is unique in combining large-scale, expert human annotation and a multi-dimensional scoring rubric, making it strong foundation for training editing reward models. More representative exam- ples from EDITREWARD DATA are shown in the Appendix AA.1 The IAA results in Table 9 prro- vide critical quantitative assessment of our expert annotation quality (Fleiss) 1971). We highlight
process are provided in the Appendix
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Data Annotation Process
LATAL STAOK
source image
Editing Medeli
Data Pool
Model
Sample
Rendom Shuffie
Instruction tollowing
BB
Madel
Quality
Pat Commnate
Termary
Qusternary
Sample
Oreel Scara
Model
Training Pipeline
Inference Pipeline
Mutti- Dimennnnnnnnnnn Unaalaiityy Aware Rarking Loss
ViLloaaadd Boward
Apgrepated Score
Strategr
Star Mars
Reward
Flend
Figure An overview of our framework, illustrating the construction of the EDITREWARD- DATA and the subsequent training of our reward model, EDITREWARD. Top: The data pipeline, where we generate a diverse candidate pool from multiple state-of-the-art models and collect multi- dimensional human preference annotations. Bottom: The model pipeline, where EDITREWARD optimized on EDITREWARD-DATE using our proposed Multi-Dimensional Uncertainty-Awer Ranking Loss for training, followed by its use in inference.
the values derived from Krippendorff's Alpha (a) (Krippendorff 2011). which is the most appropri- ate metric as it correctly models the ordinal nature of our 4-point Likert scale. The scores of 0.668 for Instruction Following (IF) and 0.597 for Visual Quality (VQ) establish a strong, quantified base- line for human consistency. Crucially, the observed difference (IAArr IAAvo) provides empirical validation for our core contribution: it confirms that the VQ dimension is inherently more subjective than IF. This validates our design choice use multi-dimensional rubric and multi-head reward model, as single holistic score would obscure this critical difference in human variance.
Table 1: The comparison of different generative preference datasets and benchmarks.
Datuset
Scale
Task Focus
Annotation
Eval. Dims.
Limitation Caveat
InageRewardDB Xu ct al.
2024
~<37
Visual Generation
Human
Single Multiple
Noise, limited diversity
VisionPrefer al. 20250
~1.2M
Visual Generation
Model
Model bias, synthetic prefs
BenAI-Benchh Jiang
X024
1166
Generation / Editing
Human
Single
Small scale
HIVE Zhangg èt al 20244
-3.6K
Instructionul Editing
Human
Single
Small reward set
ADIEF
Instructional Editing
Single
Synthetic lahels, bias
HPSv3 Ma
2025
1.
Visual Generation
Human
Single
Generalization limits
EDITRRWARD-DATA
200K
Instractional Editing
Human
Multiple
Fine eraindd supervision
Benchmark
Scale
Amnotation
Eval. Dims.
Multi-Way Preference
Pair-Wise
Point-Wise
GenAI-Beuch-Edid
2024
2-way
AURORA bond Kroye ar 2024
1..6K
Human Human
Single
2-way
ImagenHuhh Edir Ku of 2004
~ .4K
Muttiple
EDITREWARD-BH BENCH
1..5K
Cross-check
Multiple
2/34-way
Table Inter-Annotator Agreement (IAA) Metrics for Expert Annotations. The robust Krippendorff
Alpha (a) values confirm the high reliability of our multi-dimensional expert scoring.
Dataset
Fleiss' Kappa (IF)
Fleiss' Kappa (VQ)
Krippendorff Alpha (IF)
Krippendorff Alpha (VQ)
EEWAREWARD-DAC
0.4157
0.3203
0.6762
0.5720
EDDTREWARD-BENCH
0.3962
0.3157
0.6623
0.6114
All data
0.3994
0.3111
0.6685
0,5972
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Data Category Distribution
Dusaset Saurce Distriluttons
Model Contribution Disteibation
Over--laage
Reclgronn tdiliig
Sulject
Bag Deach
FLUX L-Koniiedideer
Step S0
Sukject hemocal
HAG2L//la
Nagicioohh
BAGEL
TUB
Step Leandeed
Exu Edte Twrt
Calegory Distribution
Distribution
Model Contribution Distribution
Be
white wordd d
Follwiig
Remove
bl Replace
Quiits
Modid Win
70c
color
26
Win
red
(d) Words Distribution in Dataset Instruction
(c) Human Preference Distribution
Figure 2: Statistics of EDITREWARD-DATA and EDITREWARD-BENC
2.2 THE SEETRREWARD-BECEEE CONSTRUCTION
EDITREWARD- BENCH is designed to provide more robust evaluation of image editing reward models than existing suites. We curated 500 high-quality groups from the EDITREWARD-DATA candidate pool, covering diverse editing categories. Each group was annotated by three independent experts using the same two-dimensional rubric (instruction following and visual quality) described in Section 2.1 We prioritized challenging cases where competing edits had small score differences to increase the discriminative power of the benchmark. The key innovation of EDITREWARD-BENCH multi-way preference comparison protocol that extends beyond pairwise judgments. Evalu- ation units include ternary (A, B, C) and quaternary (A, B, C. D) tuples, with correctness defined by simultaneously predicting all pairwise relations within the tuple. This strict criterion provides more comprehensive and reliable test of ranking consistency than traditional pairwise accuracy. We benchmark wide range of models EDITREWARD- BEAARRD--BCHH,.... and results reported in Section 4 More Details of the construction of EDITREWARD BENCH are provided in the Appendix A.3
EDITREWARD
3.1
ARCHITECTURE
Inspired by the success of VLMs as powerful feature extractors, we leverage a VLM the backbone for our reward model. The task of image editing evaluation is inherently tri-modal, requiring joint reasoning over source image (I,), textual prompt (P), and an edited image (L.). Our model is trained on human preference data, which consists pairs edited images, (Je,1, Ie,2), generated from the same (I P) context.
Our reward model consists of two components: a multimodal backbone, Hy (cither Qwen2.5- VL (Bai al. (023) Mimo-VL VL (Yue et al.) (0255). which computes latent representation of the edit's 's's quality; and an MLP reward head, R., which projects this representation to a scalar score. The score s, for edited image Te,s is thus given by:
(Ho (T,, P,Ta.)).
Here He represents the VLM backbone with parameters vh. and Ry is the MLP reward head with parameters w. For a preference pair, the scores 8; and $2 are computed using Eq. [ and are subse- quently used in preference loss function to jointly optimize the parameters v/ and
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3.2 MULTI-DIMENSIONE UNCERTAINTY- RANKING
Prior reward models for generative tasks often fail to account for inconsistencies in human annota- tions, treating each preference label with equal certainty. This can introduce bias, particularly when judging ambiguous or challenging cases. The HPSv3 framework (Sun et al., (025)) made significant progress text-to-image evaluation by addressing this issue. Instead of predicting a deterministic score HPSv3 models the score as Gaussian distribution ~ (N,,, d²), thereby capturing the uncertainty inherent in the data. The preference probability P(Ie,1 1 Te,2) then computed by
integrating over the two reward distributions, leading to more robust, probabilistic ranking.
Inspired by this, we adapt and extend this incertainty-awar paradigm for the more complex domain of instruction-guidee image editing. Image editing quality is multi-faceted; an edit can be faithful to the instruction but visually unrealistic, or vice versa. To capture this complexity, our EDITREWARD- DATA provides disentangled scores across two distinct dimensions: (1) Instruction Following and (2) Visual Quality. single, holistic uncertainty distribution as in HPSv3 is insufficient to model this rich, multi-dimenssonal feedback.
To this end, we adapt the reward head, Rw, using a Multi-Task Learning (MTL) Crawshaw 2020) approach. For single edited image sample (Ig, P. Ie), the reward head no longer outputs a single distribution, but rather a separate Gaussian distribution for each evaluation dimension. Let d e (1,2) represent the dimensions. The output for single sample i is pair of distributions as formulated in 2
St,d ~ WW L.a); for d = 1,
This is achieved by having the final layers of the MLP in R predict a set of parameters heads for the
115,1, M4,,, 01.2) for each input. We explore both separate and task, To train our model with this multi-dimennional output, we explore two distinct loss:
Multi- BaattiDDimesionall Uncertainty-Aware Ranking Loss. This approach extends the probabilistic ranking framework of HPSv3 (Sun et al. 2025) to our multi-dimensional task. To do so, we must first aggregate the two predicted dimensional mean scores (14,1 H4,2) for each candidate image i into a single, effective mean score, ass We propose and investigate three distinct aggregation strategies, which can be compactly formulated Eq. 3
min (p:,1 M4.2)
(Pessimistic Minimum)
2 (1 + (4.2)
(Balanced Average)
(3)
14.1 + 14.2
(Direct Summation)
The resulting aggregated means for a pair of images, along with their predicted uncertainties (o²), are then used to compute the final preference probability P(T, > I₁) following the probabilistic method from HPSv3. The model is trained by minimizing the negative log-likelihood of the ground- truth preference as in Eq.
Lrank log(P(I 1t).
Aggregated Score Regression. Alternatively, we frame the training direct regression task. This approach leverages the pointwise scores available in our EDITREWARD-DAA aggregating the predicted distributions. Given that the sum of two independent Gaussians is also Gaussian, the aggregated score distribution for a sample i is 8i.agg .N (ppi. + M4,2, o² + o.2) The model then optimized by minimizing the Mean Squared Error (MSE) between the mean of this aggregated distribution and transformed sum of the ground-tron -outh scores, Zagg = T(21 + 22):
dataset by first
Lreg Bu..PI.s,,.2 [1](4r,1 + m4,2) Lagel
(5)
This multi-dimensional uncertainty-awar approach allows our model to learn a more nuanced and disentangled representation of edit quality, leveraging the rich supervisory signal in dataset. Ablation study comparing the loss functions and aggregation strategies presented in Section 4.6
3.3 DISENTANGLING TIES VIA DIMENSIONAL PREFERENCE.
While standard models like Tradley-Terry model with ties (BTT) treat tied pairs single out- come (Liu et al. 2025a), we propose a novel data augmentation strategy to extract a richer supervi- sory signal from these ambiguous cases. Our key insight is that tie in overall quality often masks
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NUCLEAR ENGINEERING
NUCLEAR ENGINEERING
Inpructe:: begg omir
Aerevv cauuk www.ghooddd
Dewwett 2002
Reware 3.206
Figure 3: Representative examples of our reward model aligning with human judgments.
complementary dimensional strengths. For instance, one image may excel in Instruction Follow- ing while the other has superior Visual Quality. We leverage this by decomposing each qualifying tie pair (LA, L Jie into two new training samples with opposing preference labels, (LA > IB) and (IB Ia). based their respective dimensional advantages (Eq. 6). Let 24,d be the ground-truth score for image on dimension d. tie pair where one image is preterred on the first dimension and the other is preferred on the second (e.g., 2A,1 > 2 and 2B2 > 2A,2) is duplicated and relabeled
as follows:
Sample 1 with label: LA In Sample 2 with label: TB TA
The pair
(6)
This strategy forces the model to reconcile seemingly contradictory signals for the same input pair, pushing develop more granular understanding of nuanced trade-offs. This not only doubles the utility of our annotated tie data but also leads to more stable training dynamic. As illustrated in Appendix [A.7, our rie-disentanglement method results smoother training loss curve and more consistent performance gains on the validation set. Figure 3 shows some examples of our reward model giving rewards that are aligned with humans. More failure mode anaylysis of EDITREWARD shown in th Appendix A.13
4
EXPERIMENTS
4.1
IMPLEMENTATION DETAILS
We train our reward model, EDITREWARD, using 200K high-quality pairwise preference samples from our dataset. For main experimental results, report performance using two powerful lision-language models as backbones: Qwen2.5- V-LBBBBLBBBBBBB and MiMo- VL-7B. To ensure a controlled comparison, all ablation studies are conducted consistently using the Qwen2.5-VL--7B backbone. During training, all parameters of the backbone are unfrozen and set as trainable. The training is performed for 2 epochs on a cluster of NVIDIA A800 GPUs. We follow the hyperparameter configuration to HPSv3, using a learning rate of 2 x 10 with a cosine learning rate schedule and warm-up ratio of 0.05. With a per-GPU batch size of 2, the total effective batch size is 16. For preprocessing, all training images are resized to 448 x 448 pixels while preserving their original aspect ratios. Additional training details are provided in Appendix A.4
4.2 BENCHMARKS AND BASELINES
We evaluate approach suite of three established public benchmarks and newly proposed benchmark, designed to provide a more comprehensive assessment of image editing quality.
Existing Benchmarks. We utilize ImagenHub (Ku et al. 2024), GenAl-Bench (Tiang al. 2024), and AURORA-Bende (Krojer et al.) 2024). We explicitly confirm that we verified no overlap exists between our EDITREWARD: DATA training set and these evaluation benchmarks. They serve fully

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Published as a conference paper at ICLR 2026
Table 3: Comprehensive results on public benchmarks and our proposed EDITREWARD-
Under the EDITREWARD- BEDCE results, K denotes the number of candidates in the multi-way preference ranking task. Bold marks the best performance, and underline marks the second best.
EDITREWARD
SEEWARD--ENCH
Method
GenAI- Bench
AURORA-
Imagen Hub
Bench
K=2
K=3
K=4
Overall
Random
25.90
33.43
25.81
11.33
1.35
13.84
Human-te Coetanttt--iooee oo-Human
41.84
Proprietary Models
GPT-4o
53,54
50.81
38.21
45.69
27.33
7.31
28.31
GPT-5
59.61
47.27
40.85
57.53
38.51
12.84
37.81
Gemini-2.00 2.0-Flash
53.32
44.31
23,69
52,43
33.33
13.51
33.47
Nemini-2.5-Flaase
57.01
47.63
41.62
58.61
39.86
12.16
38.02
Open-Source VLMSs
Qwen2.5-VI --L-3---
42.76
30.69
-2.54
51.07
20.27
2.71
26.86
Swen2.5-VL--27 Inst
40.48
38.62
18.59
52.69
24.67
3.38
29.75
Owen2.5-VL-32B-Imss
39.28
37.06
26.87
50.54
25.27
4.05
28.72
MiMo-VL-7B-S5 VL-7B-SFT-2500
57.89
30.43
22.14
49.46
30.41
9.46
31.19
ADIEE
59.96
55.56
34.50
Reward Models (Ours)
EDITREWARD (on Qwen2.5-V1-77B)
63.97
59.50
36.18 18
56.99
36.00
10.81
36.78
EDITREWARD (on MiMo-VL-7B-SFTT
65.72
63.62
35.20
56.45
42.67
11.49
38.42
independent, held-out testbeds, ensuring fair and unbiased evaluation of EDITREWARD's gener- alization. For benchmarks with point-wise annotations like ImagenHub, we measure the Spearman rank correlation to assess alignment with human scores. For ImagenHub, which includes three rat- ings per sample, we also compute the cornan-to-Humaal correlation as a practical upper bound (Ku et al. 2023). For benchmarks with paired comparisons like GenAl-Bench and the pair-wise split of AURORA-Bende we report the prediction accuracy. Additional details of the evaluation across different methods are provided in Appendix/Ax AA.5
EDITREWARD-BENCH. Derived from the held-out test split of our EDITREWARD- -DTA dataset, this benchmark provides pair-wise preference labels. We report performance on EDITREWARD BENCH using overall preference accuracy (pair-wise). We evaluated a wide range of leading models on EDITREWARD- -ENCH to establish its utility. This included proprietary models such as GPT-4o, GPT-5, Gemini-2.00 2.0-Flas (Hassabis et al.) (2024), and Gemmini-2.5-Flan (Comanici et al. 2025). as well as prominent open-source VLMs like the Qwen2.5-VI. series and MiMo-VIL-7B. The experi- mental results, detailed in Section 4 demonstrate that EWDTREEARR-- EWARD-BENCH BENCH effectively differenti- ates between models of varying capabilities and reveals challenges, such as reasoning over multiple candidates, that are not apparent in simpler pairwise benchmarks.
4.3 EXPERIMENTAL RESULTS: ALIGNMENT WITH HUMANS
The main results presented for instruction- guided image editing. Our best model, EDITREWARD (on MiMo-VL-7B), achieves top scores on the primary public benchmarks, obtaining an accuracy of 65.72% % on GenAl-Bench and 63.62% % on AURORA-Bencis This performance surpasses strong proprietary models like GPT-5 (59.61) and the leading open-source method ADIEE (59.96) On the point-wise ImagenHub bench- mark, our model remains highly competitive with the best systems available, the Owen2.5-VL-78 variant achieves second-best Spearman correlation of 36. 18, closely following GPT-4o.
EDITREWARD
as a new state-of-the-ard reward model
Crucially, our results highlight the profound impact our training paradigm itself. By applying our methodology to the base Diven2.5-VL-7B model, we observe massive performance uplift of over 23 points on GenAl-Bench (from 40.48% to 63.97%), demonstrating that our framework dramati- cally enhances VLM's alignment with human judgments. This capability is further validated on our challenging EDITREWARD -EENAARRD-EEH,,....EEEE where EDITREWARD (on MiMo-VL-7BB again achieves the highest score of 38.42%, outperforming specialized models like Gemini-22 1.nnnni-2.5-F.- 2.5--lash (38.02) and GPT-5 (37.81). The strong performance of EDITREWAR on both Qwen and MiMo- VL backbones also confirms that framework is robust and effectively scales with more powerful base models.
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4.4 APPLICATION: EDITREWARD AS REWARD
To demonstrate EDITREWARD's practical utility as a data supervisor, conducted a data curation experiment designed to improve a state-of-the-art editing model. We employed our reward model to score the 46,000 examples in the ShareGGT-44oImmaze
dataset (Chen et al.) (025a), from which we selected high-quality subsets (Top 10K, 20K, and 30K) for comparative analysis. This curated dataset was then used to fine-tune the powerful Step IX-Edit model (Liu et al. (2025b). The com- putational cost for scoring the samples in this pool was minimal, requiring only average 2.61 GPU hours, demonstrating high efficiency (0.25 seconds/sample).
Table Comprehensive comparison of tfate-o-the--a models on both the English and Chinese ver- sions of the GEdit-Benn benchmark, across intersection and full test sets. Our model, significantly improve the base model Step X-Bdit, including sensitivity analysis for the EDITREWARD-Curated subsets (Top 10K, 20K, and 30K). f indicates higher the better. *-I means intersection set.
Bedit-Bench-EN.
GEdit-Bench-EN
GEdit-Bench-CNN
GEdit-Bench-NN
Model
G.SC
G.PQ
G.0
G.SC
G.PQ
G.O
G.SC
G.PQ
6.0
G.SC
G.PQ
G.O
AdyEdit (Yu et al. 2025
3.122
5,865
2.919
3,053
5,882
2.854
3.098
5,840
2,899
3.011
5,849
2.817
OmniGen Wu er 2025b
6.037
5,856
5.154
5.879
5,871
5.005
6.015
5,830
5.122
5.850
5.845
4.976
Gemini-2.0 Hassabis
2024
6.816
7,408
6.483
6.866
7.436
6.509
6.790
7.385
6.450
6.821
7,402
6.473
Doubuo (Wang et alg 202S0
7.396
7,899
7.137
7.222
7,885
6.983
7,370
7,870
7.105
7.195
7.851
6.942
GPT-Image-1 OpennA
7.867
8.097
7.590
7.743
8.133
7.494
7,840
8.075
7.560
7.708
8.095
7.451
Step1X XX Edit
7.289
6,962
6.618
7.131
6.99%
6.444
7.464
7.076
6.779
7.647
7.398
6.983
StepiX-Edin ShareGPT-fo- Imagg
7.411
6.838
6.803
7.349
6.893
6.780
7.126
6.855
6.595
7.116
6.807
6.583
Ours REWARR as reward) (Top-K Sensitivity)
Step1X Edit+ Ours (Top 10K)
7.762
6.811
6.957
7.690
6.866
6.938
7.591
7.064
7.000
7.591
6.987
Step1 X- X-Fiit++ Ours (Top 30K)
7,641
6,957
7.007
7.632
6,890
6.962
7.524
7.068
6.938
7.456
7.098
6.888
Step StepIX-Edit + Ours (Top 20K)
7,895
6,946
7.131
7.854
6,931
7.086
7.757
7.024
7.074
7.658
6.995
7.001
Evaluation Protocol. To measure the impact of this curation, we evaluate the resulting model on the comprehensive GEdit-Bench This benchmark features both English (EN) and Chinese (CN) instructions, as well as a challenging "Intersection" subset containing prompts that all models could process. We compare our fine-tuned model against diverse range of baselines, including the orig- inal SteptX-Edit, the same model fine-tuned on the full unfiltered dataset, and other leading open- source and proprietary models like Doubao and GPT-Image-1, 1. Following established practices Ku al.] (2023), performance is judged by GPT-4o on three metries (0- scale): Semantic Consis- tency (G (G)CC) for instruction fidelity, Perceptual Quality (G_PQ) for visual realism, and an Overall Score (G.O) for overall quality.
Results and Analysis. As detailed in Table 4 this reward-driven filtering yields significant per- formance gains. The results show a clear trade-off between data quality and volume, confirming that our EDITREWARD-illen
20K subset represents the optimal balance for fine-tuning. Our best- performing model, trained on the Top 20K subset, achieves an English G.O score of 7.086. This substantially outperforms the original Step1 X-Edit baseline (6.444) and the model trained on the full, noisy 46K dataset (6. (6.7 Furthermore, our sensitivity analysis confirms that while the Top 10K subset (representing the highest signal-to-noise ratio) also outperforms the full set (G O: 6.938), is marginally inferior to the Top 20K subset, indicating the 20K size is necessary for robust gener- alization and avoiding underfitting. Crucially, the Top 30K subset (G_0. O: 6.962) yields diminishing returns compared to the Top 20K, confirming that including lower-quality data dilutes the training signal. This finding is crucial, confirms that data quality, as judged by our reward model, is more impactful than sheer data quantity. EDITREWARD successfully prunes noisy examples that would otherwise degrade performance during finnntuning This uplift elevates the open-source StepIX- PreplXEddit to be competitive with -op-tier editors like Doubao, validating our model's 's potential as an essential tool for training gext-generation generative models.
4.5 CETTOF--DISTIBBTTNN GENERALIZATION ANALYSIS
To evaluate robustness outside the training pool, we conducted a targeted experiment on two chal- lenging Out-of-Distribuuti (OOD) categories: Text-in-Image (OCR) and Style Transfer.
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Published as a conference paper at ICLR 2026
Table 5: Accuracy comparison on OOD tasks (Text & Style) sourced from Open Images. EDITRE-
WARD achieves performance comparable to GPT-4o while being open-source and cost-effective.
Model
Text Category
Style Category
Overall
GPT-4o
45.50
35.79
41.69
EDIT REWARD (on MiMo-VL-7B-SFT)
47.83
45.41
46.80
Experimental Sctup. We constructed specialized OOD set sourced from Open Images (distinct from training sources), comprising 253 Text pairs and 185 Style pairs with expert annotations. We compare EDITREWARD (on MiMo-VL. VL--V-L-BBBBB- (-FB-SFT) against the commercial SOTA, GPT-40.
Results. Table shows EDITREWARD achieves performance comparable to GPT-4o on these tasks, maintaining competitive alignment despite inherent VLM difficulties with OCR. Crucially, EDITRE- WARD offers significant advantages as a cost-effective, open-source alternative with faster inference speeds.
4.6 ABLATION STUDIES
Table 6: Ablation study on key design choices for our reward model. We compare a point-wise regression loss (variant against our pair-wise uncertainty loss (variant II, III, IV, V), and further investigate the impact of the reward head architecture (Shared vs. Multiple) and different score aggregation strategies.
Model Configuration
Benchmark Performance
Variants
Loss Type
Head Type
Aggregation
GenAT-Bench
AURORA -Bnnhh ImagenHub
EditReward
Point-wise
N/A
N/A
49.62
42.38
13.40
22.73
Pair-wise
Shared
Mean
60.17
56.75
32.65
36.78
Pair-wise
Multiple
Min
59.96
$7.25
36.57
IV
Pair-wise
Multiple
Sum
59.63
55.19
32.93
37.60
Pair-wise
Multiple
Mcan
63.97
59.50
36.18
36.78
Ablation on Model Design. We analyze our model's key architectural choices in Table
Loss Type. Comparing loss functions (Variant vs. V), pair-wise uncertainty model (63.97 on GenAl-Bench) significantly outperforms the point-wise regression baseline (49.62). This confirms that modeling relative preferences is more effective than regressing absolute scores for this task.
Head Type. For the reward head (Variant II vs. V), using multiple independent heads (63.97) pro- vides clear improvement over shared architecture (60.17 on GenAl-Bench) suggesting that spe- cialized heads better capture our disentangled evaluation dimensions.
Aggregation Strategy. Finally, we compare three aggregation strategies (Variants III-V), find- ing that the balanced mean provides the most consistent and highest performance (63.97 on GenAI- Bench and 59.50 AURORA-Bench), We therefore adopt the Pair-wise model with Multiple heads and Mean aggregation as our final configuration.
Table 7: Ablation study on different model parameter sizes and different model backbones.
Backhone
GenAl-Bench
AURORA-Bench
ImagenHub
EDITREWARD-HENCH
Qwen2.5-VI.B2.
62.79
57.37
32.34
37.40
Qwen2.5-Vin VL-7-BB--nst
63.97
59.50
36.18
36.78
MiMo- WL-7B-SFT-255-
65.72
63.62
35.20
38.42
Ablaioon on Model Backbone, To verify our framework's generalizability. we train EDITREWARD on three backbones of varying scale and architecture, confirming that method consistently bene- fits from stronger foundation models (Table 7). Performance increases when scaling from Qwen2.5- VL-3B 8 to 7B, and improves further at the 7B scale when using the more advanced MiMo- VL.-7B

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Published as a conference paper at ICLR 2026
architecture, which achieves ofth-off scores of 65.72% GenAl-Bench and 63.62% on AURORA-Bench This demonstrates that our framework is backbone-agnostic and effectively lever- ages the capabilities of more powerful models.
RELATED WORKS
Evolution of Instruction-Guider Image Editing. Instruction-guider image editing has rapidly
evolved from early wajeectoy-bbaee methods. Diffusion models (Song et al. 2020; Dhariwal & Nichol 2021 Rombach et al., 2022 Podell al. 2023) first enabled editing via dual-prompt for-
mulations that relied on cross-attentie manipulation or inversion (Hertz et al. 2022 Mokady et al.) 2023 Wallace et al., 2023). The paradigm then shifted more user-friendly single-instruction edit-
ing. pioneered by DistructPix2Pix (Brooks et al. 2023) and refined by works like MagicBrush and Emu-Edit (Zhang et al.) 2024a (heennn al. (222)) that focused on curating high-quality datasets. This trajectory-baset family has been further advanced by flow-matching models (BlackForese Labs) et at. 2025), which improve training and sampling efficiency. In parallel, sequential generative mod- els, including autoregressive approaches (Yu et al., 2022. Tian et al. 2024), enhance compositional reasoning. The most recent advances feature hybrid multimodal architectures like OmniGen2 (Wu et al. 2025b) and BAGEL (Deng et al. 2025). which integrate large vision-language backbones with generative decoders to enable more context-aware, conversational editing.
Evaluating Instruction-Guider Image Editing. Early evaluation of image editing relied on per- ceptual metries like LPIPS (Zhang et al., 2018), but these require reference images and fail assess semantic alignment. CI.IP-based metrics (Hessel et al. (021) were introduced for text-image con- sistency but also show limited correlation with human judgment (Ku et al., 2024). The advent of large Aision-languaae
models (VLMs) enabled zero-shot evaluation, with proprictary models (Ku
et al. 2023: Wang et al. 2025b) demonstrating promising human correlation while open-source
counterparts (Liu al. 2023 Laurençon al.) 2024) have lagged (Jiang et al. (024). Conse-
quently, recent work has focused on improving open-source evaluators via fine-tuning. One strategy distills supervision from proprietary models (Wei et al., 2024. Gu et al.) 2024: Wu al. 2024).
which risks inheriting model biases. The other collects direct human annotations (Xu et al., 2024
Liang et al./ 2024] Wu et al. 2023) Sani al. (2026), offering higher-quality signals but typically at a smaller scale. Our work contributes a large-scale, expert- annotate dataset, enabling more reliable
and robust reward modeling for image editing.
CONCLUSION
In this paper, we addressed the critical bottleneck hindering the advancement of open-source instruction Instruction-guided image editing: the lack of reliable, human-aligned reward model for scaling up high-quality training data. To this end, we introduced a three-part solution: (1) EDITREWARD- DATA, new large-scale (200K) preference dataset curated with rigorous expert annotation to minimize the noise and bias prevalent in existing resources; (2) EDITREWARD, a dedicated re- ward model trained on this high-fidelity data specialize in the image editing domain; and (3) DOITREWARD-BEE BENCH, a challenging new benchmark featuring multi-way preference tasks to en- able more robust evaluation. Our experimental results validate the effectiveness of our approach. EDITREWARD establishes a new state of the art, demonstrating superior correlation with human judgment by outperforming strong VLM judges like GPT-5 and GPT-4o on public benchmarks. More importantly, we demonstrated its practical utility in a downstream data curation task: fine- tuning Step1X- Edit on a 20K subset of data filtered by EDITREWARD yielded significantly better performance than training on the full 46K noisy dataset (7.1 vs. 6.7 overall score on GEdit-Bench). This confirms that a high-quality reward signal generation editing models. Ultimately, this work provides both methodology and a set of open resources to help bridge the gap between open-source and proprietary image editing models. To empower the community and facilitate future research, we will publicly release our EDITREWARD- DATA dataset, the trained EDITR EDIRREWARD model, and the EDITREWARD -EENA- benchmark.
key ingredient for training powerful, next-
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ETHICS STATEMENT
The development of advanced instruction-guidee image editing models, which our work aims to evaluate and improve, carries significant ethical implications. While these technologies enable pow- erful creative expression, they can also be misused to generate deceptive or harmful content, such deepfakes, misinformation, or fraudulent documents, lowering the barrier for malicious actors. Our work, by creating a more effective reward model, could inadvertently contribute to acceler- ating these capabilities. We acknowledge this dual-use potential and have taken steps mitigate risks. Specifically, the EDITREWARD-DATE dataset was constructed from publicly available, non- sensitive benchmarks, and automated and manual filtering was applied to remove any personally identifiable information (PII) sensitive content. Our reward model, EDITREWARD, is trained to align with constructive and high-quality edits, as defined by our multi-dimensionall rubric, and does not follow harmful or malicious instructions. Additionally, all generated data and model outputs will be released under a CC-BY-NC-SA 4.0 license, explicitly prohibiting commercial use, which mitigates potential misuse such as the creation of deepfakes or other harmful applications. By pub- liely releasing our dataset, model, and code, we aim to promote transparency and enable the research community to further study the safety, biases, and alignment of such models. Finally, encourage the community to adopt similar safeguards, including watermarking, provenance tracking, and care- ful curation of training data, when deploying or extending instruction-guided image editing models.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we provide the following details. All of our reward models were trained on NVIDIA A800 GPUs. The evaluation of baseline models was conducted using their official public codebases and recommended configurations. For proprictary models (c.g., GPT-4o, Gemini series), we accessed their APIs between April and June 2025; given the evolving nature of these models, we have archived their specific outputs for consistency. Our new dataset, EDITREWARD- DATA, was constructed following the detailed protocol described in Section | and both the dataset and our evaluation benchmark, EDITREWARD WAITREWWRD-BENCH, will be publicly released. The complete codebase for training and evaluating our EDITREWARD, along with the final model weights for both the Qwen2.5-VI. and MiMo-VI. backhones, will be made available on GitHub and Hugging Face. Further details are provided in Appendix A.4 and A.5
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Qifan Yu, Wei Chow, Zhongqi Yue, Kaihang Pan, Yang Wu, Xiaoyang Wan, Juncheng Li, Siliang Tang, Hanwang Zhang, and Yueting Zhuang. Anyedit: Mastering unified high-quality image editing for any idea. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 26125-2613: 31, 2025.
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A APPENDIX
A.1 USE OF LLM
Large Language Models (LLMs) were used exclusively for minor grammar correction and stylistic refiement the manuseript. Their role was purely auxiliary, and all major scientific contributions were made by the authors. The authors bear full responsibility for the content of this work.
A.2 DETAILS OF HOITREWARD-DATA CONSTRUCTION
Our dataset construction centered three principles: ecological validity, by sourcing in- structions from human-vetted benchmarks; diversity, by generating candidates from state-of-the-art models; and reliability, through rigorous multi-dimensional annotation pipeline.
Source Data Collection. To ensure ecological validity, we collected 9,557 unique instruction-iman -mmage pairs from six established, human-vetted sources: GEdit-Bench (606), ImgEdit-Bente Bench (737), Mag- icBrush (1 (1,053), AnyEdit (1 (1,250), EmuEdit 15,61), and challenging internal set (300). This ag- gregation provides comprehensive foundation of semantically grounded and executable edit in- structions across a wide spectrum of tasks and styles.
Candidate Generation. For each of the 9,557 source pairs, we generated diverse pool of 12 candidate images using six otate-of-the-art models: StepIX-Edit (Liu et al. 2025b). Flux- Kontext aBlackForesttabs et al. 2025). Swen-Image-Eal (Wu et al. 2025a), BAGEL (Deng al. 2025). Ovis-UT (Wang et al. 2025a). and OmniGen2 (Wu et al. 2025b). To ensure a broad qual- ity spectrum and mitigate model-specific biases, utilized multiple random seeds, preventing any single model from dominating the candidate pool.
Table 8: The detailed comparison of different generative preference datasets and benchmarks.
Dataset
Venue
Scale
Task Focus
Annotation
Eval. Dims.
Limitation Caveat
ImageRewardDB
NeurlPS'23 23
1-33
Visual Generation
Human
Single
Expert comparisons with limited variety
Vision
NearlPS'24
Generation
Model
Multiple
Multi-aspect model-derived bias risks
GenAI-Bench
NeurlPS'2
~1.6K
Generation Editing
Haman
Multiple
High quality but very small scale
HIVE
CVPR 24
~3.6K
Instructional Editing
Human
Single
Task specific, limited comparison set size
ADIEE
10CV'25
Instructional Exiiiing
Model
Single
Synthetic labels; possible model
HPDD3
ICCV
31.1M
Visual Generation
Haman
Single
Side-spectrum: generalizabiliy limits
EDITREWARD-DAN
200K
Instructional Editing
Human
Multiple
Large scale and fine-grained supervision
Benchmark
Venue
Scale
Ansolation
Eval. Dims.
Multi-Way Preference
Pair-Wise
Point-Wise
GenAl Bench
NeurlPS'24 24
-9000
Human
Multiple
2-way
AURORA Bench
N4
~1 AK
Human
Muliiple
2-way
Imagenltub
ICLR'24
Model
Single
EDITREWARD-BENCH
Human (Cmss--chock)
Moltiple
2/34-way
Multi-Dimen Nulti-Dimensional Annotation. From the pool of 12 candidates, 7 were randomly sampled for human evaluation. Annotators provided two separate scores for each candidate on 4-point Lik- ert scale (I=Poor 4=Excellent), corresponding to our two evaluation dimensions: (1) Instruc- tion Following, which assesses semantic accuracy, completeness, and the avoidance of unprompted changes; and (2) Visual Quality, which evaluates physical plausibility, absence of artifacts, and overall aesthetic appeal. This multi-dimensional rubric provides single holistic score. Detailed interface of the annotations is in Figure annotation guidance below.
granular assessment than
We also provide detailed
Annotation Guidelines:
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Instruction Following
This dimension focuses on how accurately, completely, and exclusively the model executed the text instruction.
Key Criteria:
Semantic Accuracy: Correctly interpreting the core meaning.
Completeness: Fulfilling all parts of the instruction.
Exclusivity: Avoiding unprompted changes to the rest of the image.
Negative Indicators:
A key part of the instruction is ignored (c.g., color changed but not the object).
A major misinterpretation (e.g.. "orange" yields grapefruit..
The image unchanged random, unrelated image is generated.
Scoring Rubric (1-4 Scale):
4 (Very Good): Perfectly executes all aspects of the instruction. Edit is surgical and flawless.
3 (Relatively Good):
Achieves the main goal but with minor deviations omissions (e.g.. misses a small detail).
(Relatively Poor): Significantly misunderstands or only partially executes the instruc- tion. Unedited areas may be noticeably altered.
(Very Poor): Completely fails the instruction. The result is unrelated, or the image is corrupted.
Visual Quality
This dimension focuses on the physical plausibility, technical flawlessness, and overall aes- thetic appeal of the edited image.
Key Criteria:
Plausibility:: Consistency with real-world physics (lighting, shadows).
Artifact-Free: Absence of visual flaws (blur, distortion, seams).
Aesthetic Quality: The overall harmony, naturalness, and visual appeal.
Negative Indicators:
Obvious physical errors (e.g., an object casts no shadow).
Noticeable and distracting artifacts (e.g., blurry halo around the edit).
The final image is jarring, ugly, or unbalanced.
Scoring Rubric (1-4 Scale):
4 (Very Good): Perfectly realistic and visually flawless. The edit is undetectable and appealing.
(Relatively Good): High quality overall, but close inspection may reveal minor imper- fections (e.g., shadow is slightly off).
(Relatively Poor): The edit obvious and looks unnatural, with clear visual flaws that detract from its quality.
(Very Poor): A visual failure, full of severe errors and artifacts, making it unusable.
Quality Control. The reliability our annotations is ensured through multi-stage process. The process includes: (1) initial pilot studies to refine the annotation guidelines and rubric; (2) formal training and calibration phase for all annotators to align their judgments; and (3) continuous ran- dom sampling and cross-checking of annotations during the formal labeling process maintain
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Figure 4: Annotation Interface
Table 9: Inter-Annotator Agreement (IAA) Metrics for Expert Annotations. The robust Krippendorff
Alpha (a) values confirm the high reliability of our multi-dimensional expert scoring.
Dataset
Fleiss' Kappa (IF) Fleiss' Kappa (VQ)
Krippendorff Alpha (IF)
Krippendorff Alpha (VQ)
EDITREWARD- DATA
0.4157
0.3203
0.6762
0.5720
EDNTREWARD-
0.3962
0.3157
0.6623
0.6114
All data
0.3994
0.3111
0.6685
0.5972
high inter-anootato agreement (IAA). More representative examples from EDITREWARD -DTTA are shown in the Appendix AA.10 The IAA results in Table provide a critical quantitative assessment of our expert annotation quality (Fleiss 1971). J. We highlight the values derived from Krippendorff's Alpha (o) (Krippendorfi) 2011). which is the most appropriate metric as it correctly models the ordinal nature of our 4-point Likert scale. The a scores 0.668 for Instruction Following (IF) and 0.597 for Visual Quality (VQ) establish a strong, quantified baseline for human consistency. Crucially, the observed difference (IAA IAAvo) provides empirical validation for our core con- tribution: it confirms that the VQ dimension is inherently design choice to use a muuti-dimensione rubric and a multi-head reward model, as a single holistic score would obscure this critical difference in human variance.
subjective than IF. This validates
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A.3 DETAILS OF EDITREWARD HERNEW CONSTRUCTION
To provide a more robust and discerning evaluation of image editing reward models, we introduce EDITREWARD- BENCH. The design of this new benchmark is motivated by several limitations iden- tified in existing evaluation suites. For instance, ImagenHub utilizes a simple 3-point rating scale [0, 0.5, 1]. While user-friendly, this coarse granularity can fail to capture the nuanced quality differ- ences across the broad spectrum of semantic consistency and perceptual quality (Ku et al.] 2023). The editing tasks in AURORA-Bent instructions, which may not represent the full diversity of common editing requests.
primarily focused on action-centri and reasoning-cemme
To address these challenges, constructed EDITREWARD WERRRD-ER-- through meticulous pipeline. The foundation of our benchmark a curated subset of 500 high-quality groups sampled from EDITREWARD-DATE candidate pool, spanning distinet editing categories. To establish reliable ground truth, we engaged three independent groups of trained expert annotators. Following the multi-dimensional rubric detailed in Section 2. each annotator assigned scores on 4-point Likert scale [1, 2, 3, 4] for both instruction fidelity and visual quality, This process ensures the robustness and accuracy of our ground-truth labels. To increase the benchmark's 's difficulty and test the fine-gr fine-grained discriminative power of models, we prioritized the inclusion of samples where the competing edits have small differences their average human scores.
The primary innovation of EDITREWARD- BENCH is its introduction of a multi-way preference comparison protocol, moving beyond simple pairwise judgments. We construct more complex evaluation units, including ternary tuples (A, B, C) and quaternary tuples (A, B, C, D), based on our reliable human scores. For model's evaluation of tuple to considered correct, must correctly predict the preference relationship for all constituent pairs within that tuple (e.g.. A>B, A>C, and B>C for a ternary tuple where A is the best and C is the worst). This strict, all-or- nothing criterion provides much more comprehensive and robust measure of reward model's ranking consistency and reasoning capabilities than traditional pairwise accuracy. We evaluated a wide range of leading models on EDITREWARD- BEEENC to establish its utility. The experimental results are detailed in Section 4
Dataset Details
We provide additional details regarding our annotation protocol. All annotators followed a standardized rubric with clear dimension-specifie guidelines, covering Instruction Fol- lowing (IF) and Visual Quality (VQ). To ensure high consistency, each annotator underwent training sessions with reference examples before formal labeling.
For EDITREWARD-DATA, each edited image scored by a single expert annotator on 4-point scale (1-4) across the two dimensions (IF, VQ). This provides large-scale but fine-grained supervi- sion.
For EDITREWARD-BENCH every group is annotated by three independent experts, again along the two dimensions (IF, VQ). Annotators must jointly determine the ranking consistency among multiple candidates. When disagreements occur, a cross-check protocol ensures consistency across annotators, with the final label derived from majority agreement.
This protocol guarantees both the scale and quality of the training data and the strict reliability of the benchmark.
A.4 MORE DETAILS AND IMPLEMENTATION OF TRAINING
Reward Model Architecture. Our reward model, EDITREWARD, is built upon a powerful pre- trained Vision-Language Model (VLM) backbone, which is fully fine-tuned during training. Our main results use two backbones: Qwen2.5-VL-7B and MiMo-VL-7B. The VLM backbone is fol- lowed by Multi-Layer Perceptron (MLP) reward head. Based Multiple Head architecture, where separate MLP heads predict the parameters (n, o2) for each of the two quality dimensions independently.
ablation studies, use
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train/loss
eval/Valid Set 1_Acc
C2
3.4
0.75
1,500
Run
Smoothed
Value
Stop
Relative
Run 7
Smoothed
Value
Step
Reletive
2,500
0.7845
2,620
hr
(a) Loss curve and Valid set Acc without using Disentangling Ties via Dimensional Preference training
train/loss
F
Set
0.55
0.1
0.5
1,00
3,000
Smoothed
Stop
Relative
Run Smoothed
Value
Stop
Relative
0.5647
0.5691
4116
1,137 day
0.8179
0.8052
4,166
1,137 day
(b) Loss curve and Valid set Acc by using Disentangling Ties via Dimensional Preference training
Figure 5: Loss curve and Valid set Ace by using or not using Disentangling Ties via Dimensional Preference during model training.
A.5 MORE DETAILS ABOUT EVALUATION
We present the main experimental results in Table 3 The findings clearly demonstrate that our reward model, EDITREWARD, sets state of the art in aligning with human preferences for instruction-guider image editing.
State-of-the-Art Performance. Our best model, EDITREWARD (on MiMo-VL-7HH achieves the highest performance on three out four benchmarks. It obtains a state-of-the-art accuracy of 65.72% % on GenAl-Bench, significantly surpassing the strongest proprietary competitor, GPT-5 (59.61), and the best open-source VLM. ADIEE (59.96). Similarly, on AURORA-Benn scores 63.62%. % demonstrating a substantial margin over the next-best models, EDITREWARD (on Dwen2.5-VL-77B at 59.50% and ADIEE at 55.56%. On ImagenHub, our models remain highly competitive with the top proprietary systems, with EDITREWARD (on Qwen2.5-VL-7B)) achieving Spearman correlation of 36.18, second only to GPT-4o.
our model
Effectiveness of Reward Modeling, A key insight from our results is the profound impact of our reward modeling framework itself. By comparing the base open-source VLMs to our EDITREWARD trained on them, we can quantify the performance uplift. For instance, the base Qwen2.5-VI--7B. Inst scores 40.48% on GenAl-Bende After being trained with multi-dimensional. uncertainty- aware methodology, the resulting EDITREWARD (on Qwen2.5-VL. VL--7) skyrockets to 63.97% massive +23.5 point improvement. This demonstrates that our contribution not merely the appli- cation of a strong backbone, but a highly effective training paradigm that dramatically enhances a model's 's alignment with human judgments.
Performance on SEITREWARD-BENCEE and Backbone Generalization. Our proposed bench- mark, EDITREWARD- BENCH, proves to be more challenging and discerning testhed. Here,
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EDITREWARD (on MiMo-VL-7B) again achieves the top of 38.42%. narrowly outperform- ing Gemini-2.55-Flast (38.02) and GPT-5 (37.81). Notably, GPT-40, the best-perfornngg model on ImagenHub, scores significantly lower at 28.31, confirming that EDITREWARD- WERRERBERD- effectively identifies limitations in models that other benchmarks may miss. Finally, the strong performance of EDITREWARD on both Qwen2.5-VI. and the more powerful MiMo-VL backbone confirms that reward modeling framework is robust and can effectively leverage the capabilities of stronger base models to push the state of the art even further.
A.6 MORE DETAILS ABOUT APPLICATION
Beyond direct evaluation, a key application for powerful reward model is to improve downstream generative models through data curation. To demonstrate the practical utility of EDITREWARD, we conducted an experiment to see if it could filter large, noisy dataset to create high-quality subset for fine-tuning a state-of-the of-the-art image editing model.
Experimental Setup. Our experiment uses the open-source StepIX-Edit (Liu et al. 2025b) as the base model for fine-tuning. The training data is derived from ShareGPT-4o-Image (Chen et al. 2025a), large dataset containing approximately 46,000 instruction-image pairs. We first employed EDITREWARD to score every example in this dataset. We then curated a high-quality subset by selecting only the top-scoring 20,000 examples. The goal is to evaluate if fine-tuning X-elX-Edii on this smaller, curated subset yields better performance than training on the full, noisy dataset.
Evaluation Metrics and Baselines. We evaluate all models on GEdit-Bench a comprehensive benchmark with English (EN) and Chinese (CN) versions, each containing full set and a more challenging intersection (-I) split. Performance is measured across three axes: Semantic Con- sisteney (GLSC), which evaluates how well the edit follows the instruction; Perceptual Quality (G (Q),Q), which assesses visual realism and aesthetics; and a holistic General Overall (G 0) score. For all metrics, higher is better.
We compare our final model against two critical baselines measure the impact of our data curation:
StepIX-Edit: The original model without any additional fine-tuning.
StepIX-Edit + ShareGPT-4o-Image: The baseline model fine-tuned on the full, unfiltered PeaarePP-4oo-mman dataset.
This setup allows us to directly isolate the benefit of filtering with EDITREWARD. We also compare against other leading editing models like Doubao and GPT-Image-1 1 to contextualize our perfor- mance.
Results and Analysis. As shown in Table 4 fine-tuning Step1 1 our EDITREWARD-curated subset yields substantial improvements across all benchmarks and metrics. On the GEdit-Bench-Ii Overall score (G_O), our model achieves 7.086, significant gain over both the original Step ---eit (6.444) and the model trained on the full, noisy dataset (6. (6.780).
This result is crucial: it demonstrates that training on smaller, higher-quality dataset curated by our reward model is more effective than training on the entire noisy dataset. EDITREWARD successfully identifies and filters out low-quality or misaligned examples that can harm the fine-tuning process. Furthermore, this improvement elevates the performance of the open-source Itep1X-Edi X-Edit to be on par with, or even superior to, strong competitors like Doubao (6.983). This experiment validates the high potential of EDITREWARD an essential tool for data curation in the training pipelines of next-generation image editing models. In Figure x, we show how our reward model is used to score some image editing examples.
A.7 MORE ABLATION EXPERIMENTS RESULTS
Ablation on Data Scale and Tie Disentanglement. Next, we investigate the combined effect of increasing training data from 130k to 200k samples while also applying our proposed tie- disentanglement strategy. The results of this significant upgrade are presented in Table Compar- ing baseline model (Variant I) against our final model which incorporates both changes (Variant
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Table 10: Ablation study on dataset size and our tie-disentanglement strategy.
Ablation Setting
Benchmark Performance
Variants
Datasct Size
Discatangling Ties
GenAI-Bench
AURORA-Bench (Pair) ImagenHob
EorrREwARO-BENCH
Direct ablution
dataset
130k
62,24
$1.36
32.45
37.81
11
200k
63,97
53,33
36.18
36.78
II, we observe consistent performance gains across all public benchmarks. The improvement is most pronounced on ImagenHub, where the score increases substantially from 32.45 36.18. We also see notable gains on GenAl-Bench (62.24 + 63.97) and AURORA -eBncRh (51.36 + 53.33). Interestingly, we note a slight performance decrease on our proposed EDITREWARD-BEAE BBNCH, sug- gesting it may have different sensitivities to the data distribution. Overall, these results confirm the significant benefit of our full data strategy, which combines a larger, high-quality dataset with our novel technique for leveraging ambiguous tie pairs.
Table 11: Bias sensitivity analysis of Gemini 2.0 Flash under left/right bias conditions GenAI- Bench.
Condition
Accuracy (%)
Left Bias
55.28
Right Bias
50. 16
Bias Sensitivity (Gap)
5.11
A.8 POSITIONAL. BIAS
In the course of our evaluation on GenAl-Bench. we identified notable case of bias sensitivity in the Gemini 2.0 Flash model when subjected to systematic position bias. Specifically, when we artificially manipulated the ground-truth labels to favor either left-side (A>B) or right-side (B>A) preferences while correspondingly swapping the image positions to maintain correctness we ob- served consistent performance discrepancy. As shown in Table [1 the model achieved 55.28% accuracy under the left-bias condition but only 50. 16% under the right-bias condition, yielding a 5.11% gap. This systematic difference indicates that the model exhibits a positional preference for left-side comparisons, which could distort evaluation outcomes left unaddressed. To prevent such bias from affecting comparative results, GenAT-Bent that shuffles the order of candidate images (A and B) for each comparison task. This ensures that evaluation outcomes are driven by genuine quality judgments rather than positional artifacts, thereby preserving fairness, robustness, and reliability across diverse model architectures.
adopts randomized positioning strategy
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A.9 INPUT TEMPLATE FOR REWARD MODEL
This section provides the exact input prompt template used in all experiments to guide our reward model, EDITREWARD, in scoring the quality of an image edit.
INSTRUCTION EDIT FOLLOWING TEMPLATE
[IMAGE] You are tasked with evaluating an edited image "inn comparison with the original source image* based on ** Visual Quality & Realism** and assigning score from I to with I being the worst and being the best. This dimension focuses how realistic, artifact-free, and aesthetically appealing the edited image is, while remaining consistent with source image.
**Iaputs Provided:*s
Source Image (before editing)
Edited Image (after applying the instruction)
Text Instruction
SSub-Dimenniios Evaluate:
**Semantic Accuracy:** Assess whether the edited content accurately captures the semantics of instruction. The edited result should precisely match the intended meaning. For example, if instruction is "replace apples with oranges," the object must clearly be oranges, not other fruits. **Completeness of Editing:** Check whether **all parts** of the instruction are fully executed, For multi-step edits (e.g.. "replace red with blue bicycle"), both the color change and the object replacement must be done without omissions.
Fxxllsiivvtty Edit (No Over- Over-Editing):** Ensure only requested parts are changed. The rest of the image (as seen in the source) should remain unaltered. For example, the instruction only involves replacing an object, the background, lighting, and unrelated objects should not be unnecessarily modified.
**Scoring Criteria:**
#*4 (Very Good):** Perfectly accurate, complete, and exclusive execution of the instruction.
**3 (Relatively Good):** Largely correct, but with minor omissions or slight over-editing.
**2 (Relatively Poor):** Major misinterprctation, incomplete edits, noticeable unintended
changes.
#+1 (Very Poor):** Instruction ignored or completely wrong execution.
Text instruction
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INSTRUCTION EDIT QUALITY TEMPLATE
[IMAGE] You tasked with evaluating edited image ** comparison with the original source image based on V*sull Quality Realism** assigning a score from to with being worst and being the best. This dimension focuses on how realistic, artifact-free, and acsthetically appealing the edited image is, while remaining consistent with the souree image.
**Inputs Provided:**
Source Image (before editing)
EEited Image (after applying the instruction)
Text Instruction
**Sub Dimensions Evaluate:
**Plausibility Physical Consistency:** Check whether the edit aligns with the laws physics and the scene context. Lighting, shadows, reflections, perspective, size, and interactions with the environment should all appear natural compared to the source image.
** **Artifact-Free Quality:** Look for technical flaws such blur. distortions, pixel misalignment, unnatural textures, or seams around edited regions. High-quality results should be free from such visible artifacts.
"Aesthetic Quality:** Evaluate the overall harmony and visual appeal. The image should look natural, balanced, and pleasant. Colors, composition, and atmosphere should enhance the image rather than degrade
**Scoring Criteria:**
**4 (Very Good):** Perfectly realistic, artifact-free, seamless, and aesthetically pleasing-
**3 (Relatively Good):** Mostly realistic and clean, with only minor flaws that do not significantly distract.
**2 (Relatively Poor):* Noticeable physical inconsistencies or visible artifacts that make edit
unnatural.
++1 (Very Poor):** Severe artifacts, incoherent composition, or visually unusable result.
Text instruction (text_prompt)
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Full Input Template
[IMAGE] You tasked with evaluating an edited image **in comparison with the original source image*s, and assigning score from to 8, with being worst and being the hest. This score should reflect stthtt how accurately the instruction was followed and the visual quality of the edited image#s.
**Inputs Provided:**
Source Image (before editing)
Edited Image (after applying the instruction)
Text Instruction
**Dimension I: Instruction Following & Semantic Fidelity
Evaluate how well the edited image follows the given instruction. Consider the following sub- dimensions:
***emantic Accuracy: Check if the edited content accurately captures the intended meaning of the instruction. For example, if the instruction is "replace apples with oranges," the object must elearly be oranges, not other fruits.
**oopllttnness of Editing:** Verify that all aspects of the instruction are fully exccuted. Multi-step edits should be completely applied without omissions.
**Exllusivity of Edit (No O Ensure that only the requested changes applied; the rest the image should remain consistent with the source image without unintended modifications.
"Dimension 2: Visual Quality & Realism**
Evaluate the realism, technical quality, and aesthetic appeal of the edited image. Consider the following sub-dimensions:
**Plausibility & Physical Consistency: Check whether the edit aligns with natural laws and scene context (lighting, shadows, reflections, perspective, and object interactions). ** Artifactt Free Quality:** Assess for technical flaws such as blur, distortions, pixel misalignment, unnatural textures, or around edited regions.
** **Astthetic Quality:** Consider overall harmony visual appeal. Colors, composition, atmo- sphere, and balance should enhance the image without degrading realism.
""Scoring Criteria (1-8):**
**8 (Very Good):** Perfect instruction following and flawless visual quality; edits are accurate, complete, exclusive, and visually seamless.
**7 (Relatively Good):* Very good instruction following and high visual quality; minor, non- distracting flaws.
#*6 (Good):** Good instruction following or mostly good visual quality; minor omissions slight artifacts.
**5 (Moderate):*** Partially correct edits or moderate visual issues; noticeable flaws but understand- able.
#44 (Relatively Poor):** Significant misinterpretation, incomplete edits, noticeable visual artifacts.
**3 (Poor):*s Major emoos in instruction following and/or poor visual quality; hard to fully understand.
#*22 (Very +o Very poor edits with large semantic errors and strong visual artifacts.
++1 (Failed):** Completely wrong edits visually unusable result.
Text instruction (text_prompt)
A.10 REPRESENTATIVE RESULTS OF EDITREWARD-DATA
The following examples provide additional qualitative illustrations of EDITREWARD-DATA. They highlight broad spectrum of real editing behaviors, including appearance manipulation, object in- sertion/removal, style transfer and text change. Each example includes the source image, the edited instruction-follovinnn and visual quality. These sam-
result, and the associated annotations- ples complement the main paper by demonstrating the dataset's diversity, annotation fidelity, and coverage across both everyday and challenging editing scenarios.
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steplx v2n IF: 3 IQA::
step1x v2n random2
flux kontext IF: 4| QA:
qwen edit IF: QA: 4
IF: 3 QA:
Instruction: Replace the text 'NIPS' with 'CVPR' (a) Example from EDITRRWARD-DATA
stepix
flux kontext IF: 4 QA: 4
bagel IF: |
source
avis ul
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IF: QA:
IF: 2 QA:4
IF: 4 QA:
IF: 2 QA:
Instruction: Make the image appear it's woodblock print by Hokusai.
(b) Example from EDITREWARD--TT..
Figure 6: Representative examples from EDITREWARD-DATA. complementing Fig. 2.
A.11 QUALITATIVE EXAMPLES OF EDITREWARD-BASSE FILTERING
To provide additional intuition about the preferences learned by EDITREWARD- DATA reward model, visualize samples from the ShareGPT-44-Immm Image dataset (Chen et al. 2025a) that are either retained or filtered out after ranking with EditReward scores. The selected examples highlight the characteristic patterns captured by the reward model.
High-Quality Retained Data. Samples with high EditReward scores generally demonstrate ac- curate instruction following, clean and localized modifications, and visually coherent integration with the surrounding context. These images exhibit minimal artifacts and adhere closely to both the semantic intent and spatial constraints of the edit.
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qwen edit QA: 2
qwen odit random2
OVIs ul IF: 3 QA:
Source
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steplx IF: QA:
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IF: 2 QA:
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Instruction: change the season autumn
(a) Example from EDITREWARD DATA.
steplx v2n
OVIS
bagel think
IF: 3 |QA:3
IF: 3 QA:
IF: 4 QA:
@eplx_y2n_randons
flux kontext
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IF: 2 QA:
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Instruction: Remove the dog from the image and replace with full image cat.
(b) Example from EDTRREWARD-DATA.
Figure 7: Representative examples from EDITREWARD-DATA complementing Fig. 2.
Low-Quality Filtered Data. Samples with low scores often contain undesirable visual artifacts, incorrect or incompletely executed edits, spatial misalignment, or hallucinated content. These fail- patterns reflect typical challenges in image editing that violate instruction-followii degrade overall image quality.
Together, these qualitative examples illustrate the types of editing behaviors favored or penalized by EditReward, offering clear and interpretable view the model's learned preferences during data filtering.
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bagel
flux Kontext
qwen edit IF: 3 QA:
source
IF: 3 QA: | 4
IF: 4 QA:
bagel think
steplx
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ovis ul IF: 3 QA: 2
IF: Q|: 2
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Instruction: Add green water to the toilet bowl.
(a) Example from EDITREWARD DATA.
source
bagel think
ovis ul IF: QA: 1
omnigen2 IF: QA: 1
IF: 2 QA:2 2
bagel
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steplx
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IF: QA:
JF | Instruction: Make the spots on the closest giraffe blue.
IF: 4 QA: 2
(b) Example from EDTTREWARD-DATA.
Figure 8: Representative examples from EDITREWARD-DATA complementing Fig. 2.
A.12 QUALITATIVE COMPARISON: BEFORE vs. AFTER EDITREWARD FILTERING
To further illustrate the qualitative improvements enabled by EditReward-hasse data curation, we present side-by-side comparisons of image editing results produced by the same StepIX-Eal archi- tecture trained on two datasets: (i) the original unfiltered dataset, and (ii) the EditReward-fille high-quality subset.
For each example, we show the source image, the output from the model trained on unfiltered data (Before Filter), and the output from the model trained on EdinReward-curated data (After Filter). These examples cover a range of editing types, including background replacement, object insertion/removal, style and material changes, and human-centric edits.
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Cimiaass rabaiignund
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(a) Example List high-quality image editing samples.
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(b) Example List 2 of high-quality image editing samples.
Figure 9: Examples of qualitulity image editing samples that were retained after filtering based on EditReward scores.
Qualitatively, the "After Filter" results exhibit more accurate instruction following. cleaner local modifications, fewer visual artifacts, and more coherent global integration. In contrast, models trained on unfiltered data tend to produce incomplete edits, spatial inconsistencies, or hallucinated structures. These comparisons highlight the alignment benefits of EditReward-guidee data selection and validate its effectiveness in improving generation quality.
A. FAILURE MODE ANALYSIS OF EDITREWARD
The following analysis moves beyond generic VLM weaknesses and focuses specific, action- able biases exhibited by EDITREWARD when its judgment significantly deviates from the human consensus. We found two persistent failure modes:
1. Visual Quality Bias (Color/Brightness); We observed that EDITREWARD occasionally ex- hibits perceptual bias toward vividness, likely inherited from its VLM pre-training. The model tends to conflate high overall visual quality with excessive brightness high color sat- uration, resulting in inflated scores for edits that human judges deem over-processed or visually jarring.
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(a) Example List of low-quality image editing samples.
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(b) Example List 2 of low-quality image editing samples.
Figure 10: Examples of low-quality image editing samples that were filtered out based on EditRe- ward scores.
2. Global Consistency Failure (Background Over-Editing): Despite explicit training the "Exclusivity" criterion, the model sometimes gives high scores to edits where the background unedited regions substantially and unnecessarily altered. This suggests specific weakness in balancing local edit success against the preservation mandate.
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Published as a conference paper at ICLR 2026
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(a) Example 1: Comparison of edits produced before and after EditReward filtering.
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(b) Example 2: Another comparison of Step1X-Fath results trained with and without FedttReward filtering.
Figure 11: Qualitative comparison of Step1 X-Edit models trained on unfiltered data (Before Filter) and EditReward-curater data (After Filter). Each example shows the source image, the output before filtering, and the output after filtering. Results demonstrate that training on EditReward-fileen ffiltered data produces more accurate, stable, and faithful edits.
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Published as a conference paper at ICLR 2026
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(a) Failure Mode 1: Brightness Bias. This image received a high score from EDITREWARD (c.g., Score 3.8/4.0) due to its vivid colors, even though human experts rated it lower (e.g.. Score 2.0) for being over-saturated and visually implausible. This illustrates the model's lendency to reward excessive brightness.
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(b) Failure Mode Examplex 2: Exclusivtty/Backroond Failure. The edit (e.g.. "Change the object") was successful locally, yet the model gave it high reward (e.g.. Score 3.5/4.0) despite the background being severely altered distorted a clear violation of "no unprompted changes" rule.
Qualitative Taxonomy of EDITREWARD'S Reward Biases. These examples illustrate specific cases where EDITREWARD'S high scores deviate from human consensus, demonstrating the model's inherited bias toward high color vividness and its difficulty in penalizing subtle global inconsistencies.
Figure 12:
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Reviews:
[{"review_id": "o5IWJKuLdo", "reviewer": "Reviewer_7xvL", "summary": "This paper proposes EditReward, a vision-language model (VLM)-based reward model tailored for image editing tasks, aiming to better align with human preferences regarding editing outcomes. The authors construct a large-scale, high-quality human-annotated preference dataset, train an EditReward model based on VLMs, and introduce a new, more challenging multi-choice preference ranking benchmark.", "questions": "See Weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. EditReward-Data is currently one of the largest and most diverse preference datasets in the image editing domain, offering significant practical value.\n2. EditReward achieves state-of-the-art or near-SOTA performance across multiple public and newly proposed benchmarks.\n3. The paper is clearly written, and the figures effectively illustrate the methodology and workflow.", "weaknesses": "1. The paper evaluates only Instruction Following and Visual Quality, but overlooks the preservation of unedited regions, which is a critical aspect of image editing. Many edits may unintentionally alter details outside the target area. \n2. VLMs often struggle with fine-grained spatial reasoning, yet image editing demands pixel-level precision. How does EditReward mitigate potential inaccuracies when evaluating spatially sensitive edits (e.g., object positioning, resizing, or local geometric changes)?\n3. Using VLM-based approaches as reward models incurs substantial inference overhead, limiting practical deployment.\n4. Although the paper mentions quality control procedures for human evaluation (e.g., training, calibration, spot-checking), it does not report inter-annotator agreement metrics, making it difficult to assess the reliability of the annotations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hKwqFPHn3U", "reviewer": "Reviewer_iwg3", "summary": "This paper introduces EditReward, a specialized reward model for  instruction-guided image editing, designed to help open-source models catch up with proprietary giants. The authors argue the main bottleneck is the lack of high-quality training data. Their solution is to first build a massive dataset with over 200,000 expert-annotated preference pairs, uniquely scored on both \"Instruction Following\" and \"Visual Quality\". They use this to train EditReward, a reward model that shows superior alignment with human preferences, outperforming even models like GPT-5 on several benchmarks. Critically, they demonstrate its practical value by using it to filter a noisy dataset, significantly improving a downstream editing model's performance. The work provides a new dataset, model, and benchmark to accelerate open-source development.", "questions": "1.  The \"tie-disentanglement\" trick for training is very clever. Does this teach the model that some dimensions are more important than others in \"tie-breaker\" situations? For example, could it learn to favor an image with better visual quality over one with slightly better instruction following when the overall scores are similar?\n\n2. Given that expert annotation is a bottleneck, have you considered using Edit reward itself in a semi-supervised or self-training loop to label a much larger, un-annotated dataset, effectively creating a \"student\" reward model that learns from your expert one?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. High-Quality, Large-Scale Human Data. It introduce a 200K expert-annotated dataset which is a massive undertaking and a huge contribution to the community. \n2. Instead of a single score, separating \"Instruction Following\" from \"Visual Quality\" is brilliant. It provides a much richer, disentangled signal for the model to learn from, which is crucial for editing tasks where these two aspects can be in conflict.\n3. A More Challenging Benchmark(EditReward-Bench) with multi-way ranking is a forward-thinking move. It pushes the evaluation standard higher, forcing models to be more consistent and discerning.", "weaknesses": "1. The Cost of Quality: The whole project relies on \"trained experts.\" This is expensive and hard to scale. While they've created a great resource, it doesn't solve the fundamental problem of how to continue generating high-quality preference data affordably.\n\n2. Are two dimensions enough? \"Instruction Following\" and \"Visual Quality\" are great, but what about more abstract concepts like \"creativity,\" \"style preservation,\" or \"compositional harmony\"? Complex edits might require more than two axes to be judged properly.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "39swNOhasc", "reviewer": "Reviewer_i644", "summary": "Paper: EDITREWARD: A Human-Aligned Reward Model for Instruction-Guided Image Editing.\nContributions: The authors (1) construct EDITREWARD-DATA—a 200K expertly annotated, multi-dimensional human preference dataset for instruction-guided image editing; (2) train EDITREWARD, a VLM-based reward model with a multi-dimensional, uncertainty-aware ranking loss that predicts Gaussian scores per dimension (Instruction Following, Visual Quality); (3) introduce EDITREWARD-BENCH, a stricter evaluation with multi-way (K=2/3/4) preference ranking; and (4) demonstrate a data curation application where EDITREWARD filters a noisy dataset to improve a strong editor (Step1X-Edit) beyond training on the full set. SOTA results are reported on GenAI-Bench and AURORA-Bench; competitive on ImagenHub; strongest on the proposed EDITREWARD-BENCH (Table 2). The pipeline and data construction are summarized in Fig. 1 and §2; loss and training details in §3.2–§3.3; ablations in Table 4–5; curation results in Table 3.", "questions": "Tool-swap robustness: How does EDITREWARD perform if you replace the VLM backbone with a different family (e.g., Idefics2 or LLaVA-Next) and/or swap the image encoder? Can you report Δaccuracy on Table 2 metrics under such swaps? \nAggregation policy: Can you provide use-case-conditioned aggregation (e.g., pessimistic min for safety-critical; mean for general editing) with calibrated thresholds, and report trade-offs on Table 2? \nAnnotator agreement: Please report IAA (e.g., Spearman or Krippendorff's α) per dimension for EDITREWARD-DATA and EDITREWARD-BENCH to bound the attainable ceiling and compare to \"Human-to-Human\" lines (Table 2). \nOOD editing types: Have you tested style-centric or text-in-image/OCR edits and failure rates? If not, could you include a small OOD slice or note expected behavior? \nCuration efficiency: For Table 3, what are the compute costs (GPU-hours) of scoring and filtering 46k examples, and how sensitive is the improvement to the top-K cutoff (e.g., 10k vs 20k vs 30k)?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "High-quality human supervision at scale. EDITREWARD-DATA provides ~200K expert preference pairs with two-dimension rubric; construction protocol and statistics are detailed in §2 and Fig. 2/Table 1—a step up from noisy, pseudo-labeled datasets. \nPrincipled reward modeling. The Gaussian per-dimension scoring and uncertainty-aware pairwise loss (Eq. 2–5) are well-motivated, capturing ambiguity and disentangling \"follow the instruction\" vs. \"visual quality.\" The tie-disentanglement augmentation further leverages ambiguous labels (§3.3, Fig. 5). \nStrong empirical validation. New SOTA alignment with human preferences on GenAI-Bench (65.72) and AURORA-Bench (63.62); best overall on EDITREWARD-BENCH with multi-way tuples (Table 2). \nActionable downstream impact. Using EDITREWARD to filter ShareGPT-4o-Image and fine-tune Step1X-Edit yields higher GEdit-Bench overall (7.086 vs 6.780 full-set), showing quality>quantity for editing training data (Table 3). \nTransparent ablations and generality. Loss/head/aggregation comparisons (Table 4), backbone scaling (Table 5), and dataset/tie studies (Table 7) indicate robustness and portability.", "weaknesses": "Tool/backbone dependence not fully stress-tested. While two VLM backbones are tried (Qwen2.5-VL-7B, MiMo-VL-7B), broader tool-swap sensitivity (alternative VLMs, different visual encoders, cropping policies) is missing; this matters because reward transferability across labs/tooling is crucial (cf. Table 5 limited scope). \nBenchmark coupling and generalization. EDITREWARD-BENCH is curated from the same candidate pool as training sources (§2.2). Although splits are held-out, more OOD editing distributions (e.g., style-centric edits or long-tail compositional instructions) would better test generalization. Style edits are known gaps in related evaluators and are not emphasized here. \nAggregation choice/per-axis reporting. The mean aggregation across dimensions often performs best (Table 4), but policies like pessimistic min may align better with certain safety-critical uses. A task-conditioned aggregation or calibrated composite is not explored. \nHuman-label reliability bounds. The paper describes expert protocols and cross-checks yet gives few quantitative IAA statistics for EDITREWARD-DATA (A.2). Providing per-dimension IAA would contextualize achievable upper bounds (as done for ImagenHub). \nLimited analysis of failure modes. Qualitative examples exist (Fig. 3), but a taxonomy of consistent reward failures (e.g., counting, fine geometry, subtle semantics) with confusion patterns would guide future reward design.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "e4DmjP6hjW", "reviewer": "Reviewer_1h4X", "summary": "This paper presents EDITREWARD, a human-aligned reward model for instruction-based image editing. The authors build EDITREWARD-DATA, a 200K-scale preference dataset annotated along two dimensions (instruction following and visual quality), train a vision–language-based reward model with a multi-dimensional uncertainty-aware ranking loss, and propose EDITREWARD-BENCH for evaluation. EDITREWARD achieves strong results on GenAI-Bench, AURORA-Bench, and ImagenHub, and shows practical utility by improving data curation for training image editors.", "questions": "1. Were any samples from the test splits of the original benchmarks (e.g., MagicBrush or GEdit-Bench) included in EDITREWARD-DATA’s training pool?\n2. Did the authors verify that EDITREWARD-DATA does not overlap with GenAI-Bench, AURORA-Bench, or ImagenHub (in images, prompts, or instructions)?\n3. Could the authors share inter-annotator agreement statistics to quantify annotation reliability?\n4. Could the authors clarify the licensing and redistribution permissions for EDITREWARD-DATA, given that it combines multiple public datasets (e.g., MagicBrush, AnyEdit, EmuEdit) that may have differing usage terms?\n5. Could the authors provide qualitative visual comparisons (e.g., side-by-side edited images) showing how edits differ when the same model architecture is trained on (a) the original dataset and (b) the EDITREWARD-curated subset? This would help readers understand the qualitative nature of the claimed alignment improvements.\n6. Could the authors provide more details about the human annotation process, including the number of annotators, their training and demographic diversity, and how they were compensated? Were any measures taken to ensure diverse perspectives or reduce annotator bias in the preference judgments?\n7. Could the authors include more representative examples from EDITREWARD-DATA (e.g., a sample of editing instructions, before/after images, and associated annotations) to illustrate dataset diversity and label consistency?\n8. Since the paper uses the reward model for data ranking and filtering, could the authors show qualitative examples of what types of samples are retained or filtered out by EDITREWARD to provide intuition about its learned preferences?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Addresses an important and timely challenge: aligning open-source image-editing models with human preference.\n- Introduces EDITREWARD-DATA, a high-quality 200K, pair dataset with expert human annotations.\n- Proposes a multi-dimensional uncertainty-aware ranking loss, a thoughtful extension of prior single-dimension reward modeling.\n- Demonstrates strong results across several public benchmarks and a practical downstream benefit for data curation.\n- Writing, figures, and comparisons are clear and thorough.", "weaknesses": "- **Data reuse and potential leakage.** Section 2.1 states that EDITREWARD-DATA is constructed from several existing editing benchmarks (GEdit-Bench, ImgEdit-Bench, MagicBrush, AnyEdit, EmuEdit). Because these benchmarks contain test splits in their original form (see also Fig. 2b), it remains unclear whether EDITREWARD’s training pool includes samples originating from those test sets. Clarifying this is critical to ensure that the reward model was not exposed to data overlapping with any public evaluation benchmarks. A simple leakage or overlap analysis would strengthen the claims of fair evaluation.\n- **Benchmark independence.** The paper reports results on GenAI-Bench, AURORA-Bench, and ImagenHub, which appear to be distinct from the training sources. However, an explicit statement confirming the absence of overlap (in images or prompts) between EDITREWARD-DATA and these benchmarks would improve transparency.\n- **Limited discussion of annotation reliability.** Although the authors mention a rigorous protocol and Likert-scale ratings, quantitative inter-annotator agreement or consistency metrics are not reported. Including such statistics (e.g., Cohen’s κ) would reinforce the dataset’s claimed quality.\n- **Lack of qualitative visual comparison.** While the quantitative metrics show consistent gains, the paper would benefit from direct visual comparisons between edits generated using datasets curated by EDITREWARD and those curated by other reward models or full unfiltered datasets. Such examples would make the improvements more interpretable and tangible, especially for assessing human alignment.\n- **Limited analysis of failure cases.** The experiments report strong quantitative results but give little qualitative analysis of where the model fails (e.g., when human judgment and reward disagree). Showing even a few representative failure patterns would strengthen interpretability.\n- **Reproducibility details.** Training parameters are described briefly, but access to scripts or validation splits would be essential to reproduce results once the dataset is released.\n- **Data annotation transparency and demographics.** Although the authors state that annotations were collected by “trained experts” following a standardized protocol, the paper provides no concrete details about the number of annotators, their demographic or institutional backgrounds, training procedures, or compensation. These factors are critical for assessing annotation quality, potential bias, and fairness. Greater transparency here would strengthen the dataset’s credibility and ethical grounding.\n- **Limited dataset examples and analysis.** Since EDITREWARD-DATA is a major contribution, the paper would benefit from showing more representative examples from the dataset, including the range of editing instructions, image types, and annotation cases. This would help readers better understand dataset diversity, annotation granularity, and potential biases. A few visual samples or statistics beyond Fig. 2 would strengthen the dataset description.\n- **Lack of qualitative illustration of the filtering process.** The paper describes reward-guided data selection but does not provide examples of what EDITREWARD retains or rejects. Visual examples of this filtering effect would make the model’s behavior and alignment properties much clearer.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)