Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking
Authors: Wen Wen
tianwu zhi
Kanglong FAN
Yang Li
Xinge Peng
Yabin ZHANG
Yiting Liao
Junlin Li
Li zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Self-evolving
image quality assessment
vision-language model
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce EvoQuality, a novel framework that enables a VLM to autonomously refine its quality perception capabilities without any ground-truth labels.

Abstract: Improving vision-language models (VLMs) in the post-training stage typically relies on supervised fine-tuning or reinforcement learning, methods that necessitate costly, human-annotated data.  While self-supervised techniques such as self-consistency have proven effective for enhancing reasoning capabilities, their application to perceptual domains such as image quality assessment (IQA) remains largely unexplored.  In this work, we introduce EvoQuality, a novel framework that enables a VLM to autonomously refine its quality perception capabilities without any ground-truth labels.  EvoQuality adapts the principle of self-consistency to the ranking-based nature of IQA.  It generates pseudo-labels by performing pairwise majority voting on the VLM's own outputs to establish a consensus on relative quality.  These pseudo-rankings are then formulated into a fidelity reward that guides the model's iterative evolution through group relative policy optimization (GRPO).  By iteratively leveraging its own predictions, EvoQuality progressively refines the VLM’s perceptual capability. Extensive experiments show that EvoQuality boosts the base VLM's zero-shot performance by 31.8% on PLCC across diverse IQA benchmarks. Remarkably, despite being entirely self-supervised, EvoQuality achieves performance that is competitive with, or even surpasses, state-of-the-art supervised VLM-based IQA models, outperforming these models on 5 out of 7 IQA benchmarks. Furthermore, the framework demonstrates significant flexibility, allowing it to be stacked with pre-trained IQA models to bolster generalization on unseen datasets.

Published as a conference paper at ICLR 2026
SELFIEGOLVING VISION-LANGUAGE MODELS FOR IMAGE QUALITY ASSESSMENT VIA VOTING AND RANKING
Wen Wen't
Tianwu Zhi
Kanglong Fan'
Yang Li
Xinge Peng
Yabin Zhang 24
Yiting Liao²
Junlin Li
Li Zhang
City University of Hong Kong
"ByteDance Inc.
ABSTRACT
Improving vision-language models (VLMs) in the post-training stage typically relies supervised fine-tuning reinforcement learning, methods that necessi- tate costly, human-annotated data. While self-supervised techniques have proven effective for enhancing reasoning capabilities, their application to perceptual do- mains such as image quality assessment (IQA) remains largely unexplored. In this work, we introduce EvoQuality, novel framework that enables VLM autonomously refine its quality perception capabilities without any ground-truth labels. EvoQuality adapts the principle of self-coonsstenncy to the ranking- ranking-based nature of IQA. It generates pseudo-labels by performing pairwise majority voting on the VLM's own outputs to establish a consensus on relative quality. These pseudo-rankings are then formulated into fidelity reward that guides the model's iterative evolution through group relative policy optimization (GRPO). By itera- tively leveraging its own predictions. EvoQuality progressively refines the VLM's perceptual capability. Extensive experiments show that EvoQuality boosts the base VLM's zero-shot performance by 31.8% on PL.CC across diverse IQA bench- marks. Remarkably, despite being entirely self-supervised, EvoQuality achieves performance that competitive with, even surpasses, site-offthhearr super- vised VLM-based IQA models, outperforming these models on out of 7 IQA benchmarks. Furthermore, the framework demonstrates significant flexibility, al- lowing it to be stacked with pre-trained IQA models to bolster generalization on unseen datasets.
TID2013
PIPAL
KADID
LIVEW
1.0
AGIQA
+28.9%
KADID
+77.4%
+46.2%
Q-Alige
PL.CC
0.6
01,933
SPAQ
Q.lnight
0.5
VasalQuadiy
0.674
IL649
Our
0.623
0.444
0.2
CSIQ
Basellne
TID2013
(a)
(b)
Figure 1: Without any ground truths, EvoQuality enables Qwen2.5-VL-7BB to self-evolve its IQA capabilities, achieving (a) substantial performance improvements over the baseline and (b) superior or competitive results compared to supervised VLM-based models across multiple IQA benchmarks.
Equual Contribution. Correspondence Wen Wen (wwen29-c@mycccruudc...h Project Lead.

---

Published as a conference paper at ICLR 2026
INTRODUCTION
Vision-language models (VLMs) have demonstrated remarkable progress across a wide spectrum of tasks, from high-level vision applications such as image captioning and visual reasoning (Liu et al.,
2023 Zhu et al., 2023; Li et al. 2023b; Wei et al. [023] Wang et al. 2024 Bai et al. 2025) low-
level tasks including image quality assessment (IQA) (Wu et al. 2024a c). Despite these advances,
VLMS often exhibit instability and inconsistency, even for identical inputs, due to variations model architectures, decoding strategies, and task complexities. To address these limitations, post-training has become a critical step in improving reliability and accuracy for downstream tasks. Existing approaches are predominantly based on supervised fine-tuning (SFT) and reinforcement learning (RL) with verifiable rewards. For instance, recent work has shown that online RL methods such as GRPO (Shao et al., 2024). combined with task task-specific verifiable rewards (Guo al.)
2025 Zhou
ct al. 2025 Meng et al. [0255] Deng et al. 2025 Li et al., 2025b Wu el al. 2025), ), can substantially enhance the reasoning capabilities of both large language models (LLMs) and VLMs.
Motivated by the increasing scale and capability of foundation models (Zhang et al. 2026). recent work has turned toward self-supervised post-training strategies that exploit the internal model out- puts without reliance on ground-truth annotations. A prominent example is self-consistency (Wang 2022), which leverages the "wisdom of the crowd" effect within the model itself. Instead of relying on a single prediction, self-consistency guides the model to generate multiple diverse reasoning traces for the same problem. The final answer determined by aggregating these out- puts, often through majority voting. The underlying intuition is that correct solutions are likely to emerge consistently across diverse reasoning paths, whereas incorrect ones are more sporadic. This paradigm has shown notable effectiveness: TTRL (Zuo et al. 2025 employs majority voting to improve mathematical reasoning in LLMs, MM-UPT (Wei et al. 2025) adapts it to VLMs for geo- metric moblem-solvvng balance accuracy and efficiency. Collectively, these advances highlight the potential of combining online RL with self-rewarding, enabling models to refine themselves without human supervision.
and Fu et al. (2025) demonstrates entropy entropy-based weighted voting to further
IQA aims to estimate perceptual image quality in alignment with human judgment. However, ob- taining ground-truth labels prohibitively costly, as it requires large-scale subjective studies. While some prior approaches (Gu et al. 2019) have employed off-the-shelf full-reference metrics (Wang al. 2004: Zhang a (2014) generate pseudo-labels. these methods are inherently restricted to synthetic datasets and their performance remains bounded by the capabilities of the selected metric. As a result, existing IQA datasets are limited in scale and diversity, constraining the generaliza- tion ability of supervised models and underscoring the need for self-supervised training paradigms. Nevertheless, extending self-evolving approaches IQA poses unique challenges. Unlike discrete reasoning tasks, IQA is inherently continuous and subjective: there is no absolute correct score; instead, reliable evaluation relies on relative comparisons across images. This makes the design of effective suservuperiiionn signals non-trivial. Recognizing this characteristic. recent IQA meth- ods have incorporated learning-to- to-rank strategies, demonstrating strong performance (Zhang et al. 2021 Zhu et al. 2024 You et al., 2025 Wu et al., 2025). Therefore, adapting self-consistency to IQA requires addressing both how to aggregate votes and how to establish robust relative rankings.
In this paper, we present EvoQuality, fully self-supervised framework that enables VLMs to pro- gressively evolve their understanding of image quality via self-consistent voting and ranking. The framework operates in two stages. In the offline stage, the VLM performs pairwise comparisons and applies majority voting to produce high-confidence pseudo-labels that capture relative quality. In the online stage, these wveudo-rankings are transformed into fidelity rewards, which guide model updates through group relative policy optimization (GRPO). This iterative process can be repeated and allows the VI.M to continuously improve its perceptual capabilities without reliance on human annotations. Extensive experiments show that EvoQuality significantly advances the base VL.'' zero-shot capabilities, improving performance by 31.8% on PLCC across diverse IQA benchmarks covering authentic, synthetic, and Al-generated distortions. Crucially, without access to any ground- truth labels, EvoQuality achieves performance that surpasses state-of of-the--rtt supervised VLM--baee IQA models. By validating the efficacy of self- celiisonsist, voting and ranking paradigm, Evo- Quality establishes a new and scalable approach for building robust IQA models, paving the way for reliable quality assessment in domains where human annotations are scarce entirely unavailable.
2

---

Published as a conference paper at ICLR 2026
2
RELATED WORK
2.1 SELI-EVOLVING METHODS
Large language models (LLMs) often rely on alignment techniques, such as reinforcement learn- ing from human feedback (RLHF) (Ouyang al. 2022), to improve their performance. However, the high cost and scalability limitations of human annotation have led to the development of self- evolving methods, which allow models to autonomously enhance their capabilities using unlabeled data. A central approach within this framework is to derive rewards from the model's own out- puts. One notable method is based on consensus and self-consistency (Wang et al., 2022), where the model generates multiple candidate solutions for given task, and the agreement between these solutions serves as pseudo-reward. This approach has been successfully applied to enhance the rea- soning abilities of LLMs in domains such as mathematics and code generation, with methods like TTRL (Zuo al. 2025), MM-UPT (Wei et al. 2025), and SRT (Shalayat al. 2025). Specifically, MM-UPT further strengthens a model's 's math and geometry abilities by generating synthetic geom- etry problems. Alternative approaches involve using a powerful model as an automated judge (Pang
et al.
2024) or leveraging the model's 's internal states, such as confidence scores (Zhao et al. 2026
ct al.
2025a) or the entropy of its output distribution (Zhang et al., 2025). Recently, DeepConf (Fu
ct al.
2025) has leveraged the entropy of tokens to enhance reasoning efficiency and improve per-
formance at test time.
2.2 IMAGE QUALITY MODELS
Regression-based models. The conventional approach to NR-IQA has been regression, where the goal is to predict absolute quality score for given image. Early methods relied on hand-crafted features derived from natural scene statistics (Mittal et al. 2012a b) or distortion-specific cues. The paradigm later shifted to deep learning, where end-to-end neural networks were trained to map image pixels directly to a quality score (Kang et al. 2014; Talebi & Milanfar 2018 Ke et al. 2021 Yang al. 2022). This regression framework has recently been extended to VLMs. For instance, methods like Q-Align (Wu et al. 2024b) treat IQA as an ordinal task, training the VLM to generate tokens representing distinct quality levels which are subsequently converted to numerical scores, while others like Q-Insight (Li et al.] (0255) pursue direct score regression. However, regression- based models often exhibit limited generalization and require complex perceptual scale realignment when trained on multiple datasets with different rating scales (Mikhailiuk et al., (021).
Ranking-baseee models. To overcome the limitations of absolute scoring, ranking- -baee models reframe IQA as a relative task, positing that comparative judgments are more robust and consis- tent with human perception. This paradigm was pioneered by adapting
coaning-to-ranke algorithms like RankNet (Burges et al. (2005), RRLRIQA (Gu et al. 2019), and psychometric models like the Thurstone model (Thurstone] 1927) to the IQA problem (Ma et al.) (207). By training on pairs of images, these models learn to predict which image has better quality. The fidelity loss, in par- ticular, has been shown to be highly effective for this task (Tsai et al. 2007| Zhang et al. [02T). Recently, this ranking philosophy has been successfully applied to VLMs, leading to models such as 2ompere2Score (Zhu et al.; 2024). DeQA-Score (You et al., 2025). and VisualQuality- R1 (Wu) al. 2025), which leverage the rich representations of VLMs for relative quality assessment. Our proposed method, EvoQuality, builds upon this powerful ranking paradigm. Moreover, it takes a significant step forward by eliminating the dependency on human-annotated labels, instead enabling the model to autonomously evolve its understanding of relative quality through a self-supervised voting and reward mechanism.
3 EVOLVING IMAGE QUALITY ASSESSMENT
This section details the design of EvoQuality, the self-evolving IQA framework, as shown in Fig- ure Inspired by self-consistency and the inherently "relative" nature of IQA, our method combines pairwise majority voting with ranking to provide a paradigm specifically tailored for IQA tasks. The core of EvoQuality is an iterative, two-step loop: 1) an offline stage where a VLM generates pseudo- ranking labels through pairwise voting, and 2) an online stage where the VLM's policy is updated
3

---

Published as a conference paper at ICLR 2026
Pair-i Response I
Paírwise Pseudo-labels
chink- compare two images, analyae Bhem step by step: 1, Color Lightig and Exgusuie I.. Jelhink Usnoe
Policy Model
Offiine
Voling
Pseudo Ranking
Pair-i Response
Label Pool
<hink-Boli images uppear quality
observations: Clarity Coker Fideligg silinn hoccal)))
Pair-l
Pair-N
Fidelity
Reward
Img-i Response
Predicted
<hink> This image is close suon of hone mesdowl.... -thini- (boxed
Scores
k-th Estimati
Policy Model
and Variance
Online
Img-I Response
<hink- This image contans hoese, grassland skyl... -iinni bboxed
Thurstone
Img-i Response
Predicted
Model
Img-1
Img-2
Img-N
<thnn- Ther is vills in this imagn, with bise grassaral) -iiiinc (3.5
arietly with the index of better image.
Offiine Prompt: <irage--cimgee Answer
Img-j Response K
...
Online Prompt: <image> Whar is your
<hink- This inage lostance house with patch s-hiink Banod (3.90
Mean Estimate
overall miing on the quality of this picture.
and Variance
Figure 2: System diagram of the proposed self-evolving IQA framework EvoQuality, Each iteration operates in two stages. In the offline stage, the VLM generates pscudo-ranking labels for unlabeled image pairs via pairwise majority voting. In the subsequent online stage, the VLM's policy is up- dated via GRPO (Shao et al. 2024) using fidelity reward derived from these pseudo-labele This iterative loop enables the VLM self-evolve its understanding of image quality.
using these self geferated labels as reward signal. This loop allows the model to progressively refine its perceptual judgment.
3.1 OFFLINE PSEUDO-LABEL GENERATION PAIRWISE MAJORITY VOTING
The offline stage in each evolution eycle to generate a reliable supervisory signal from batch of unlabeled images { 22. .22....,IN' .N). Using the current VLM policy and comparative prompt Ccompure (see Table T). we perform pairwise comparisons on a set of random selected image pairs P ((I,, x. ........ To ensure self-consistency, we query the policy Tp (-I 2₁, 22,) K times for each pair and determine the preference by majority voting. Besides, to mitigate positional bias in prompts, we randomly permute the order of image pairs.
Table 1: Structured prompt Coompare for offline pairwise voting and prompt Goooe for online score generation, together with the reasoning suffix.
Geompare <image>><<>e image> You are performing an image quality assessment task. Compare the two images and decide which one has better perceptual quality. Answer strictly with the index the better image: 0 if the first image is better, or if the second image is better.
C <image> You are doing the image quality assessment task. Here is the question: What is your overall rating on the quality of this picture? The rating should be float between 1 and rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality.
Suffix: You FIRST think about the reasoning process internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </ </think> tags. The final answer MUST BE pul in boxed ).
Unlike methods that aggregate votes into continuous quality scores, we directly record the outcome of each comparison. This process yields of high-confidence pairwise pseudo-labels, denoted as p (1,)) for each pair (x,, P. The value is p (x) 1 if image 24 is determined

---

Published as a conference paper at ICLR 2026
Algorithm EvoQuality
Input: Unlabeled image pairs P = Tt),........ from unlabeled images (01, T2 IN current
VLM policy væ. reference VLM policy Tet voting budget K, number of batches M, batch size B, maximum round Т;
Output: Updated VLM policy mp:
1: for all - to T do for all image pair (z,) in P do
2:
D Offline Stage
3:
Query T- Gcumpare 2i, xj) K times
4:
Perform majority voting to generate pseudo preference p (z1, (Eq. end for
5:
6: Sample M batch inputs B = P, where |Bmll
Online Stage
7:
for all m to M do
8:
for all image pairs (I,, z2)) in batch B,, do
9:
Query Te x;²) and m=(-(escores K times
Compute the fidelity reward FA (1,) with pseudo labels {p" (z;, 2t (), (Eq.
Swap the position of x; and 2y to compute the fidelity reward rx (x (Eq.
12:
Compute relative advantages {ax (z;) ]== and {ax (z;)) from the rewards (Eq,
13:
Update policy Ta using the objective {(6, Ter (Eq.
end for
15: end for
end for
to be better, p (x,, = 0 Of xy is better, and (xx,, = 0.5 for a tie:
if K > K p (τ, v) 0.5 if K2 = Ky if K, K
where K2 is the total count of votes for image x being better, and Ky is the total count of votes for image y being better. This set of definitive pairwise outcomes, derived purely from the model's internal consensus, completely replaces the need for external human mean opinion score (MOS) labels and serves as the "ground truths" for the subsequent online training stage.
3.2 ONLINE POLICY EVOLUTION WITH FIDELITY REWARDS
In the online stage, we fine-tune the VLM policy using the pseudo-labels generated during offline voting as the sole source of supervision. For each image x, from the offline compari- son, we use a direct estimation prompt Cscare (Table D Using the policy from the previous it- eration, Te.a (scoore, we sample K reasoning trajectories and corresponding quality scores fox; qo (x:))) k- The vector of scores is denoted g(x;) =
(x:))
Motivated by Thurstone model (Thurstone) 1927) and VisualQuality-Re (Wu al. 2025), we com- pute the model's predicted comparative probability II)) for each of the pairs (2;, P that were evaluated in the offline stage:
((xx;)-(((<)))) (c(((())) (3(((z;)) +
Pa (T,,, =
(2)
where (+()) is the standard Gaussian cumulative distribution function. u(g(z;)) and (3((x)) represent the mean and variance of the quality predictions for w.,, respectively. 7 is small positive constant to avoid any potential division by zero.
The core of our self-supervised learning is the reward function rı (2:)) We adapt the fidelity measure by replacing any ground-truth preference with pseudo-label preference (I,, (x,,,,) from Eq. The reward is calculated only over the of images P: (ary((2,, E PP that were paired with

---

Published as a conference paper at ICLR 2026
Table 2: PLCC and SRCC results of EvoQuality, reported together with percentage improvements (%) over the baseline model Qwen2.5- VL--B. WAVG. means the weighted average result, with the weighting proportional to the number of images in each dataset.
Owen2.5-VLL-77
EvoQuality @ Round1
EvoQuality @ Round2
Dataset
PL.CC
SRCC
PL.CC ())
SRCC (A %)
PI.CC ())
SRCC (A%% %)
KONIQ
0.761
0.703
0.840(+10.49)
0.794(+12.9%)
0.835(+ 9.7%)
0.791(+12.5%)
SPAQ
0.853
0.843
0.902 5.7%)
0.899 (+ 6.6%)
0.903 5.9%)
0,900 (+ 6.8%)
AGIQA
0.766
0.681
0.839 (4 9.5%)
0.7.7(+14.1%%
0.831 8.5%%
0.771(+13.2%)
LIVEW
0.718
0.700
0.847(+18.0%)
0.814(116.3%)
0.847(+18.0%)
0.813(+16.1%)
KADID
0,623
0.587
0.784(+25.8%)
(33.2%))3)2%)
0.803(428.9%)
0.807(+37.5%)
PIPAL
0.444
0.388
0.613(338.19)
(+4.5%)
0.649(+46.2%)
0.583(+50.3%)
TID2013
0.380
0.363
0.624(+64.2%)
0.587(+61.7%)
0.674(+77.4%)
(.6.3))
CSIQ
0.717
0.656
0.8.7)+
(.789++00.3%)
0.865(+20.6%6
0.839(427.9%)
WAVG.
0.615
0.570
0.751(+27.49)
0.709(429.6%)
0.770(+31.8%)
0.726(+33.7%)
I during the offline voting stage:
Th (xx)
1 P.
Σ
v(1 (1--1 p² (1i,,;;))(1 pa (xi,,
This reward signal provides precise guidance for the policy to align with its own aggregated judg- ments. We collect K fidelity rewards for x; into the vector r(æ;) (=;)...... (x;,))] and compute the relative advantage (z;) by standardizing rewards within the group:
ag (x(x;) rx(x;)) ((((x.))
(4)
o(r(z;))
The policy update of NO C- 2;)) follows the regularized GRPO:
K
To(onIc,2;))
((0, (TT) = BK
min
To (ox(c, On (x:). clip Tea (onle,
e, + an(ozs)
3 DEL (molonle, (o (rooloolesx))lladdoolll..::
(5)
where B denotes the batch size during online training. The reference policy Tee x;) is fixed, (- (-Ic,2;) is the policy from the previous iteration, used to sample K reasoning trajectories o= = {on) The KI. divergence term is approximated as:
while Te.
2e log To(ox|c,z;)
Treronke, No (o mo(on|c,20 c.,.)
DKL (oolool..z;))lmmecoll
(6)
which constrains Te(-le, 2;)) from diverging excessively from Te (le, a.)). The clipping threshold
prevents unstable policy updates, while the coefficient, 3 balances the reward-weighted likelihood and KI. regularization terms.
The whole algorithm is shown in Algorithm This two-stage loop allows the model to progressively strengthen its understanding of image quality by iteratively generating and learning from its own evolving consensus.
4 EXPERIMENT
4.1 EXPERIMENTAL. SETUPS
Datasets. EvoQuality is trained exclusively on the KONIQ dataset (Hosu et al. 2020), augmented with synthetically generated distortions. Following YYouet 2024 create 10 distorted variants

---

Published as a conference paper at ICLR 2026
Table PLCC and SRCC results of NR-IQA models trained on KONIQ. The proposed EvoQuality and handcrafted ones DO NOT access the ground truths. The top-2 results are highlighted in bold and underline. WAVG. means the weighted average result across the out-of-distribution datasets.
Method
KONIQ
SPAQ
AGIQA
LIVEW
KADID
PIPAL
TID2013
CSIQ
WAVG.
PLCC
Handcrufted
NIQE
0.533
0.679
0,560
0.493
0.499
0.372
0.548
0.751
0.520
BRISQUE
0.225
0.490
0.541
0.361
0.442
0,412
0.337
0,749
0.449
Discriminative Deep-Learning-baaee based
NIMA
0.896
0.838
0.715
0.814
0.701
0.536
0.418
0.854
0.641
HyperIQA
0.917
0.791
0.702
0.772
0.555
0.511
0.169
0.816
0.560
DBCNN
0.884
0.812
0.730
0.773
0.562
0.469
0.008
0.682
0.521
CONTRIQUE
0.798
0.651
0.679
0.713
0.590
0.416
0.055
0.518
0.476
Re-IQA
0.197
0,578
0.671
0.354
0.361
MUSIQ
0.924
0.868
0.722
0.789
0.668
0.567
0.257
0.621
CLIP-IQA+
0.909
0,866
0.736
0.832
0.762
0.550
0.293
0.900
0.641
MANIQA
0.849
0.768
0.723
0.849
0.526
0.603
0,172
0.712
0.582
VLM-based
Q-Align
0.941
0.886
0.772
0.853
0.795
0.600
0.282
0.814
0.663
DeQA-Score
0.953
0.895
0.809
0.891
0.711
0.608
0.192
0.868
0.652
Qwen2.5-VL-77
0.703
0.853
0,766
0.718
0.623
0.444
0,380
0.717
0.598
Q-Insight
0.928
0.893
0.818
0,870
0,759
0.608
0.483
0,759
0.704
VisualQuality-R 11
0.882
0.877
0.817
0.856
0.723
0.531
0.435
0.768
0.667
EvoQuality @ Round
0,840
0.902
0.839
0.847
0.784
0.613
0.624
0.827
0.740
EvoQuality Round2
0.835
0.903
0.831
0.847
0.803
0.649
0.674
0.865
0.762
SRCC
Handcrafted
NIQE
0.530
0.664
0.533
0.449
0.430
0.120
0.110
0.645
0.349
BRISQUE
0.226
0.406
0.497
0.313
0.374
0.279
0.115
0.551
0.333
Diseriminative Deep-Learning based
NIMA
0.859
0.856
0.654
0.771
0.486
0.616
HyperIQA
0,906
0.788
0.640
0.749
0.554
0.756
0.510
DBCNN
0.875
0,806
0.641
0.755
0.551
0.408
0.653
0,490
CONTRIQUE
0.754
0.632
0.606
0.676
0.437
0.341
0.069
0.318
0.411
Re-IQA
0.838
0.174
0.525
0.624
0.303
0,281
0.096
0.244
0.301
MUSIQ
0.929
0.863
0.630
0.830
0.650
0.535
0.248
0.815
0.592
CLIP-IQA+
0.895
0.864
0.685
0,805
0.756
0.499
0.309
0.890
0.618
MANIQA
0.834
0.758
0.636
0.832
0.488
0,551
0.116
0.675
0.534
VLM-bused
Q-Align
0.940
0.887
0.735
0.860
0.792
0.518
0.284
0.750
0.631
DeQA-Score
0,941
0.895
0.729
0.871
0.716
0.565
0.132
0.847
0.614
Qwen2.5-VL-78
0.761
0.843
0.682
0.700
0.587
0.388
0.363
0.656
0.554
Q-Insight
0.909
0.891
0.760
0.839
0.762
0.534
0.468
0.714
0.667
VisualQuality-R RI
0.852
0.878
0.760
0.827
0.719
0.486
0.388
0.707
0.631
EvoQuality Round1
0.794
0.899
0.777
0.814
0.782
0.545
0.587
0.789
0.699
EvoQuality Round2
0.791
0.900
0.771
0.719
of each original image by randomly sampling 10 out of 35 distortion types and 5 severity levels. To
enforce fully supervieedd setting, all ground truth information is discarded, including both the quality scores from KONIQ and the labels of distortion types and severities. Thus, EvoQuality treats the training corpus as an unlabeled image collection for its evolutionary process.
For evaluation, we conduct zero-shot testing on seven benchmarks: 1) authentic distortions:
SPAQ (Fang al. 2020), LIVEW (Ghadiyaram & Bovik 2013); 2) Al Al-generated distortions: AG- IQA [Li et al. 2023a): 3) synthetic distortions: CSIQ (I Larson & Chandler 2010). TID2013 (Pono-
marenko al. 2015), KADID (Lin et al. 2019), and PIPAL. (Gu et al. 2020).
Competing methods. We compare the zero-shot generalization performance of EvoQuality against comprehensive suite of NR-IQA models, including: 1) traditional handcrafted methods, NIQE (Mittal et al.) (2012b) and BRISQUE (Mittal et al. 2012a); ; 2) discriminative deep-learning- based models: NIMA (Talebi & Milanfar, (2018), 8). HyperIQA (Su et al., (2220), DBCNN (Zhang et al. al. (022), Re-IQA (Saha et al. 2023), MUSIQ (Ke al. 2021. J CLIP-IQA+ (Wang et al.) (023). and MANIQA (Yang et al. 2022); 3) VLM-based mod-
2018), CONTRIQUE (Madhusudana
els: Q-Align (Wu et al. 2024b). DeQA-Score (You al. 2025). Q-Insight (Li al. 2025b) and

---

Published as a conference paper at ICLR 2026
Table PLCC and SRCC results VLM-based models co-trained on KONIQ, SPAQ. and KADID
datasets. The top-2 results are highlighted in bold and underline. WAVG. means the weighted
average result across the -iittr-disttiuttion datasets.
Method
KONIQ
SPAQ
KADID
AGIQA
LIVEW
PIPAL
TID2013
CSIQ
WAVG.
PLCC
in-distribution
ouf-of-distribution
Q-Align
0.945
0.933
0.977
0.788
0.887
0.603
0.684
0.924
0.714
DeQA-Score
0.957
0.938
0.967
0,808
0,900
0.597
0,806
0.940
0.748
VisualQuulity-RI
0.915
0.902
0.935
0,815
0,869
0.606
0.703
0.899
0.722
EvoQuality
0.795
0.899
0.796
0.822
0.818
0.645
0.678
0.887
0.727
VQRI+ QuvoQuality
0.915
0.916
0.938
0.830
0.874
0.669
0.772
0.926
0.768
SRCC
in-distribution
dottof-ddstribbution
Q-Align
0.938
0.931
0.974
0.733
0.883
0.577
0.676
0.910
0.688
DeQA-Score
0.944
0.934
01.665
0.745
0.887
0.552
0.752
0.914
0.700
VisualQuality-RI
0.899
0.903
0.931
0.752
0.850
0.554
0.633
0.839
0.666
EvoQuality
0.731
0.897
0.785
0.754
0.785
0.579
0.667
0.848
0.678
VQ-RI Qu-ulityy
0.899
0.917
0,936
0.779
0.851
0,610
0.715
0.885
0.716
VisualQuality-11 (Wu et al. 2025 and the pre-trained Qwen2.5-VL-7BB (Bai et al. 2025 which serves as our baseline.
Implementation details. We conduct EvoQuality in two iterations (7 = 2): the first uses only authentic image pairs from KONIQ, while the second incorporates synthetically generated images, with pairs randomly sampled under the same reference. In the offline stage of each iteration, 20,000 image pairs are randomly constructed, and pseudo-labels are obtained by prompting Qwen2.5-VL- 7B (Bai et al. 2025) with prompt Coompare in Table T to generate 32 responses (K' 32). followed by majorty voting. In the online stage, the entire VLM is fine-tuned with GRPO (Shao et al. 2024),
I Training employs Adam (Loshchilov & Hutter 2017) with initial learning rate of 3 x 10 a linear decay schedule, and balance coefficient 3 0.05. Experiments are conducted on eight NVIDIA A100 GPUs with a per-GPU batch size of four, requiring about twelve hours per epoch.
again generating 32 responses with prompt Cecore in Table
4.2 RESULTS
Significant improvement over the baseline. As shown in Table 2 EvoQuality delivers consis- tent and substantial improvements over the Qwen2.5-VI--B baseline across all eight IQA bench- marks. After just a single evolutionary iteration (EvoQuality @ Roundl), the model achieves notable gains, with the weighted average PLCC and SRCC increasing by 27.4% and 29.6%, respectively. Moreover, the second iteration (EvoQuality @ Round2) furthur improves the weighted average per- formance by 31.8% and 33.7%. These results confirm the effectiveness of the self- supervised voting and ranking in enhancing the perceptual judgment of the base VLM.
Superiority
supervised IQA models. A key strength of EvoQuality lies in its exceptional generalization across diverse datasets. Despite being trained exclusively on unlabeled images from the KONIQ dataset, the model demonstrates robust performance on seven unseen test sets, covering a wide spectrum of content and distortion types, as shown in Table
First, on weighted average across all benchmarks, our final model, EvoQuality @ Round2, sur- passes the leading supervised model, Q-Ingghtt, by more than 0.05 on PLCC and SRCC. Second, this robustness is particularly evident on the challenging TID2013 dataset, where all learning-base methods struggle. EvoQuality achieves a PLCC score over 0.15 higher than the next-best performer NIQE. Third, EvoQuality shows notable gains on the AGIQA dataset, which contains Al Al-generated content. Unlike prior supervised models that fail to generalize to such novel content, our method achieves substantially superior performance, demonstrating its capacity to adapt to emerging image types. Furthermore, EvoQuality outperforms VisualQuality-Pe ment learning with pairwise fidelity rewards from ground truths. As observed in Table 3, although EvoQuality does not match supervised VLM-based models on the in-domain KONIQ dataset, its superior cross- dataset performance indicates that the proposed self-evolving paradigm reduces over- fitting and enhances generalization.
a method that leverages reinforce-
8

---

Published as a conference paper at ICLR 2026
Table 5: PLCC performance comparison of EvoQuality and EvoEstimate across two evolutionary rounds. The best results are highlighted in bold. A indicates an improvement of at least 0.01 in the second round over the first.
EvoQuality
EvoEstimate
Dataset
Roundo
Round1
Round2
A
Round1
Round2
A
KONIQ
0.761
0.840
0.835
X
0.844
0.802
X
SPAQ
0.853
0.902
0.903
0,902
0.902
X
AGIQA
0.766
0.839
0,831
0,841
0.841
LIVEW
0.718
0.847
0.847
0.819
0.815
X
KADID
0.623
0,784
0,803
0.744
0.753
X
PIPAL
0.444
0,613
0.649
0.624
0.633
X
TID2013
0.380
0.624
0,674
0.552
0.550
CSIQ
0.717
0.827
0,865
0.843
0.840
WAVG.
0.615
0.751
0.770
0.738
0.736
Progressive refinement with more iterations. As shown in Table 2 the second iteration (Evo- Quality@Roumat @Round2), which incorporates synthetic images, further boosts performance, raising the weighted average PLCC and SRCC improvements to 31.8% and 33.7% over the baseline. The largest second-round gains appear on datasets with synthetic distortions, such as TID2013 (PLCC rising from +64.2% to +77.4%) and PIPAL (PLCC rising from +38. 1% to +46.2%). Crucially, although the performance gains on authentic datasets are marginal, the model maintains its previous standards and does not suffer from catastrophic forgetting. These results highlight that EvoQuality 's evolutionary process can progressively refine the model's capacity to handle wider spectrum of image distortions.
Enhanced sensitivity to synthetic distortions. EvoQuality demonstrates particularly strong perfor- mance datasets with challenging synthetic distortions, outperforming other VLM-based models, as shown Table Since these ground truths are obtained under controlled double-stimulus set- tings, the results indicate that EvoQuality's 's self-evolution by pairwise voting and ranking captures a more robust and generalizable model of perceptual quality. This confirms the effectiveness of learning relative preferences from self-generated signals for handling complex image degradations.
Augmenting pre-trained IQA models for unseen generalization, To further validate the flexibil- ity of our framework, conducted experiments following the multi-dataset co-training protocols of prior works (Wu et al. 2024b. You et al.] (2025), utilizing the KONIQ, SPAQ, and KADID datasets, as shown in T While EvoQuality naturally trails supervised methods on in-distribution perfor- mance due to the absence of ground-trutt truth labels, its primary advantage lies in its additive capability. EvoQuality can be applied not only base VLMs but also to pre-trained, supervised IQA models. As demonstrated in Table 4 when using the pre-trained VisualQuality-RI Quality further boosts out-of-distribution performance. This unique characteristic underscores the practical applicability of EvoQuality in real-worlds scenarios, where it can be employed to enhance the generalization of existing models to unseen domains.
the base model, Evo-
4.3 ABLATIONS
Ablation on self-evolving variants. To validate our choice of a canking-based framework, we introduce EvoEstimate, a variant EvoQuality. Unlike the proposed framework, which learns from relative quality comparisons, EvoEstimate is designed to predict direct, absolute quality score. EvoRstimate vote and generate the score directly in the offline stage using the prompt Creare- The multiple voting results are then average to calculate the pseudo estimations. In the online stage, EvoEstimate is trained with the pscudo labels using the rewards as Q-Insight (Li et al. (025b), treated IQA direct regression task. All other settings remain the same with EvoQuality. The comparison results are shown Table 5
authentic image datasets, EvoQuality demonstrates significantly stronger performance on benchmarks with synthetic distortion datasets. This suggests that learning from relative rankings provides more robust signal for understanding complex image
Although EvoEstimate is competitive

---

Published as a conference paper at ICLR 2026
K-8
K-16
K-32
0.85
0.525
0.540
01.00
0.395
0.85
0.835
0.844
0.539
0.85
0,8440
0.836
0.547
0.80
0.90
0.761
0.85
0.766
0.75
0.75
0.75
0,718
0.70
0.80
0.70
0.70
KONIQ
SPAQ
AGIQA
LIVEW
0.80
0.768
0:784
0.60
0.609
0.614 0.613
0.60
0.848
0.813
0.75
0,525
0.525
0.70
0.50
0.50
0.65
0.464
0.40
0.380
KADID
PIPAL
TID2013
CSIQ
Figure 3: PLCC results of EvoQuality with varying K candidate responses.
artifacts than regressing to an averaged pseudo-score. Furthermore, EvoQuality continues to im- prove in the second evolutionary round by 0.02 on PLCC, whereas EvoEstimate shows almost progress. These results indicate that pairwise voting and ranking provides a more reliable learning signal and enables improvements, while direct regression limits further evolution.
Effect of candidate response K. To analyze the effect of the number of candidate responses used for voting, we conducted an ablation study by varying K {1, 16, 32), while all other hyper- parameters remained fixed. The case where K = represents a baseline without any consensus mechanism. As depicted in Figure 3 the introduction of voting process (K 1) yields signifi- cant performance improvement over the baseline. The results further indicate that larger number of candidates generally leads better outcomes, with 32 demonstrating the most stable and robust performance across the diverse IQA benchmarks.
CONCLUSION AND DISCUSSION
Conclusion. We have introduced EvoQuality, fully self-supervised framework that enables VLM to self-improve its IQA capability. By integrating pairwise majority voting with relative ranking strategies, EvoQuality generates robust supervisory signals, allowing the model to refine its perceptual judgments directly from internal consensus. Comprehensive experiments validate its effectiveness, demonstrating significant gains in the VLM's performance across diverse IQA bench- marks. Furthermore, EvoQuality demonstrates remarkable flexibility: it can be seamlessly applied atop existing supervised VL.M-asedd models augment generalization unseen datasets, thereby establishing its practical value for real-world deployment.
Limitations and future directions. Despite the promising results, EvoQuality has limitations that suggest clear directions for future research. First, although the framework eliminates the reliance on ground-truth quality labels, it remains contingent upon an in-domain pool of unlabeled images. The diversity and scope of this unsupervised corpus may directly influence the VLM's ultimate performance. Future work could investigate methods enhance data efficiency or enable effec- tive learning from more generalized, out-of-domain image collections. Second, the self-evolution process, particularly the iterative voting and reinforcement learning stages, entails significant com- putational overhead. We posit that substantial optimizations are feasible. Future research could focus on developing more efficient voting schemes, such as active sampling strategies that prioritize the most informative image pairs. Finally, we envision extending the EvoQuality paradigm to other subjective perceptual tasks where relative ranking is intrinsically more suitable than absolute regres- sion, such assessing image aesthetics, evaluating the realism of generative models, ranking user-generated videos. Collectively, these directions highlight the potential for developing robust, scalable, self-evolving perceptual systems.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, al. Qwen2.5-VL. technical report. arXiv preprint arXiv:2502. 13923, 2025.
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hul- lender. Learning to rank using gradient descent. In International Conference on Machine Learn- ing. pp. 89-96, 2005.
Yihe Deng, Hritik Bansal, Fan Yin, Nanyun Peng, Wei Wang, and Kai-Wei Chang, Open VLThinker: Complex Assion-language reasoning via iterative SFT-RL cycles. In Anmual Conference on Neural Information Processing Systems, pp. 1-16, 2025.
Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. Perceptual quality assessment of smartphone photography. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3677-3686, 2020.
Yichao Fu, Xuewei Wang, Yuandong Tian, and Jiawei Zhao. Deep think with confidence. In NeurlPS Workshop on Efficient Reasoning, pp. 1-13, 2025.
Deepti Ghadiyaram and Alan Bovik. Live in the wild image quality challenge database. http://fiive. ece. utexas. eddiresaarciCChallenngeBBB
html, 2015.
Jie Gu, Gaofeng Meng, Cheng Da, Shiming Xiang, and Chunhong Pan. No-reference image quality assessment with reinforcement recursive list-wise ranking. In Proceedings of the AAAI Confer- ence on Artificial Intelligence, volume 33, pp. -336-8333, 2019.
Jinjin Gu, Haoming Cai, Haoyu Chen, Xiaoxing Ye, Jimmy S. Ren, and Chao Dong. PIPAL: A large- scale image quality assessment dataset for perceptual image restoration. In European Conference Computer Vision, pp. 633-651, 2020.
Daya Guo, Dejian Yang, Haowei Zhang. Junxiao Song. Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang. Xiao Bi, et al. DeepSeck-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501. 2025.
Vlad Hosu, Hanhe Lin, Tamas Sziranyi, and Dietmar Saupe. KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Pro- cessing, 29:4041-4000
2020.
Le Kang, Peng Ye, Yi Li, and David Doermann. Convolutional neural networks for no-reference im- age quality assessment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1733-1740, 2014.
Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. MUSIQ: Multi-scale image quality Transformer. In /EEE/CVF International Conference on Computer Vision, pp. 1148--157, 2021.
Eric C. Larson and Damon M. Chandler. Most apparent distortion: Full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging. 19:1-21, 2010.
Chunyi Li, Zicheng Zhang, Haoning Wu, Wei Sun, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, and Weisi Lin. AGIQA-3K: An open database for Al-generated image quality assessment. IEEE Transactions Circuits and Systems for Video Technology. 34:6833-6846 2023a.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International Conference on Machine Learning, pp. 19730-19742, 2023b.
Pengyi Li, Matvey Skripkin, Alexander Zubrey, Andrey Kuznetsov, and Ivan Oseledets. Confidence is all you need: Few-shot RL fine-tuning of language models. arXiv preprint arXiv:2506.0639; 2025a.
11

---

Published as a conference paper at ICLR 2026
Weiqi Li, Xuanyu Zhang, Shijie Zhao, Yabin Zhang, Junlin Li, Li Zhang, and Jian Zhang. Q-Insight: Understanding image quality via visual reinforcement learning. In Annual Conference on Neural Information Processing Systems, pp. 1-14, 2025b.
Hanhe Lin, Vlad Hosu, and Dietmar Saupe. KADID- 10K: A large-scale artificially distorted IQA database. In IEEE International Conference on Quality of Multimedia Experience, pp. 1-3, 2019.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Annual Conference on Neural Information Processing Systems, 36:34892-34916, 2023.
Iyya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Confer- ence on Learning Representations, pp. 1-19, 2017.
Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, and Dacheng Tao. diplQ: Blind image quality assessment by learning-to-Fni to-k discriminable image pairs. IEEE Transactions on Image Process- ing, 26:3951-3964, 2017.
Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli, and Alan C. Bovik. Image quality assessment using contrastive learning. IEEE Transactions on Image Processing, 31:41499 4161, 2022.
Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, Ping Luo, Yu Qiao, Qiaosheng Zhang, and Wenqi Shao. MM-Fureka: Exploring visual aha moment with rule-based large-scale reinforcement learning. arXiv preprint arXiv:2503.07365, 2025.
Aliaksei Mikhailiuk, María Pérez-Ortiz, Dingcheng Yue, Wilson Suen, and Rafat K Mantiuk. Con- solidated dataset and metrics for high-dynamic-range image quality. IEEE Transactions on Mul- timedia, 24:2125-2133 2021.
Anish Mittal, Anush Krishna Moorthy, and Alan C. Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 1146695-7088 2012a.
Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. Making "completely blind" image quality analyzer. IEEE Signal Processing Letters, 00:209-212, 2012b.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kel- ton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback. In An- nual Conference on Neural Information Processing Systems, pp. 27730-27744 2022.
Jing- Cheng Pang, Pengyuan Wang, Kaiyuan Li, Xiong- Hui Chen, Jiacheng Xu, Zongzhang Zhang, and Yang Yu. Language model self-improvement by reinforcement learning contemplation. In International Conference Learning Representations, pp. 1-11, 2024.
Nikolay Ponomarenko, Lina Jin, Oleg leremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica Battisti, and Kuo C.-C. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication, 30: 57-77, 2015.
Avinab Saha, Sandeep Mishra, and Alan C. Bovik. Re-IQA: Unsupervised learning for image quality assessment in the wild. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5846-5855, 2023.
Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, Jeff Schneider, and Andrea Zanette. Can large reasoning models self-train? arXiv preprint urXiv:2505.2144 2025.
Zhihong Shao, Peiyi Wang, Qihao Zhu, Rumxin Xu, Junxiao Song. Xiao Bi. Haowei Zhang, Mingchuan Zhang, YK Li, Wu, and Daya Guo. DeepSeekMath: Pushing the limits of mathe- matical reasoning in open language models. arXiv preprint arXiv:2402.00 03300, 2024.
12

---

Published as a conference paper at ICLR 2026
Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. Blindly assess image quality in the wild guided by a self-adaptive hyper network. In IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pp. 3667-3676, 2020.
Hossein Talebi and Peyman Milanfar. NIMA: Neural image assessment.
IEEE Transactions on
Image Processing, 27:3998-4000 4011, 2018.
Louis L. Thurstone. law of comparative judgment. Psychological Review, 34:273-286, 1927.
Ming-Feng Tsai, Tie- Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A ranking method with fidelity loss. In International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 383-390, 2007.
Jianyi Wang, Kelvin C.K. Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2555-2563, 2023.
Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang. Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang. Wenbin Ge, et al. Qwen2-VL: Enhancing vision-lange -anguuge model's perception world at any resolution. arXiv preprint arXiv:2409.12I1 121911 2024.
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoe Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In International Conference on Learning Representations, pp. 1-24, 2022.
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 13:600- 612, 2004.
Lai Wei, Zihao Jiang, Weiran Huang, and Lichao Sun. InstructionGPT-44 A 200-instruction paradigm for fine-tuning minigpt-4. arXiv preprint arXiv:2308.12065 120667 2023.
Lai Wei, Yuting Li, Chen Wang, Yue Wang, Linghe Kong, Weiran Huang, and Lichao Sun. First SFT, Second RI,, Third UPT: Continual improving multi-modal LLM reasoning via unsupervised post-training. In Annual Conference on Neural Information Processing Systems, pp. 1-16, 2025.
Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang. Chunyi Li, Wenxiu Sun, Qiong Yan, Guangtao Zhai, and Weisi Lin. Q-Bench: benchmark for general- purpose foundation models on low-level vision. In International Conference on Learning Repre- sentations, pp. 1-13, 2024a.
Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guangtao Zhai, and Weisi Lin. Q-Align: Teaching LMMs for visual scoring via discrete text-defined levels. In International Conference on Machine Learning, pp. 54015 54029, 2024b.
Tianhe Wu, Kede Ma, Jie Liang, Yujiu Yang, and Lei Zhang. A comprehensive study of multimodal large language models for image quality assessment. In European Conference on Computer Vi- sion, pp. 143-160, 2024c.
Tianhe Wu, Jian Zou, Jie Liang, Lei Zhang, and Kede Ma. VisualQualityRRI: Reassning-induced image quality assessment via reinforcement learning to rank. In Annual Conference Neural Information Processing Systems, pp. 1-13, 2025.
Sidi Yang, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujiu Yang. MANIQA: Multi-dimension attention network for no-reference image quality as- sessment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp- 1191-1200, 2022.
Zhiyuan You, Zheyuan Li, Jinjin Gu, Zhenfei Yin, Tianfan Xue, and Chao Dong. Depicting beyond scores: Advancing image quality assessment through multi- modal language models. In European Conference Computer Vision, pp. 259-276, 2024.
13

---

Published as a conference paper at ICLR 2026
Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, and Chao Dong. Teaching large language models to regress accurate image quality scores using score distribution. In JEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14483-14494, 2025.
Lin Zhang, Ying Shen, and Hongyu Li. VSI: visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10))4270-2281 2014.
Qingyang Zhang. Haitao Wu, Changqing Zhang, Peilin Zhao, and Yatao Bian. Right question is already half the answer: Fully unsupervised IIm reasoning incentivization. Annual Conference on Neural Information Processing Systems, pp. 1-13, 2025.
Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, and Zhou Wang. Blind image quality assessment using a deep bilinear convolutional neural network. IEEE Transactions on Circuits and Systems for Video Technology, 30(1):36 2018.
Weixia Zhang, Kede Ma. Guangtao Zhai, and Xiaokang Yang. Uncertainty-aware blind image quality assessment in the laboratory and wild. IEEE Transactions on Image Processing, 30:3474- 3486, 2021.
Zicheng Zhang. Junying Wang, Yijin Guo, Farong Wen, Zijian Chen, Hanqing Wang, Wenzhe Li, Lu Sun, Yingiie Zhou, Jianbo Zhang, Bowen Yan, Ziheng Jia, Jiahao Xiao, Yuan Tian, Xi- angyang Zhu, Kaiwei Zhang, Chunyi Li, Xiaohong Liu, Xiongkuo Min, Qi Jia, and Guangtao Zhai. AlBench: Towards trustworthy evaluation under the law Displays, 91:103255, 2026. ISSN 0141-9382.
Xuandong Zhao, Zhewei Kang, Aosong Feng, Sergey Levine, and Dawn Song. Learning to reason without external rewards. In International Conference on Learning Representations, pp. 1-14, 2026.
Hengguang Zhou, Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, and Cho-Jui Hsieh. R1- zero's "aha moment" in visual reasoning on a 2b non-sft model. arXiv preprint 25X1522503.05132, 2025.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. In International Conference on Learning Representations, pp. 1-15, 2023.
Hanwei Zhu, Haoning Wu, Yixuan Li, Zicheng Zhang, Baoliang Chen, Lingyu Zhu, Yuming Fang, Guangtao Zhai, Weisi Lin, and Shiqi Wang. Adaptive image quality assessment via teaching large multimodal model to compare. Annual Conference on Neural Information Processing Systems, 37:32611-32629 2024.
Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, Biqing Qi, Youbang Sun, Zhiyuan Ma, Lifan Yuan, Ning Ding, and Bowen Zhou. TTRL: Test-time reinforcement learning. In Annual Conference Neural Information Processing Systems, pp. 1-14, 2025.
A APPENDIX
B THE USE OF LARGE LANGUAGE MODELS (LLMS)
We utilized LLMs to assist in improving the grammar, clarity, and overall readability of this manuscript. All scientific claims, experimental results, and core ideas were conceived and artic- ulated by the human authors. The authors have reviewed and edited all LLM-generated suggestions and take full responsibility for the final content of this paper.
14


Reviews:
[{"review_id": "wRXwnWegh3", "reviewer": "Reviewer_Mtc1", "summary": "This paper proposes EvoQuality, a self-supervised framework for enhancing VLMs in opinion-unaware IQA. It features two key design elements. First, leveraging the LLM’s self-consistency to generate pseudo-labels indicating the relative quality of image pairs. Second, establishing a self-evolving mechanism that progressively refines the VLM’s perceptual capability by iteratively alternating between pseudo-label generation and model training. Experiments on seven IQA benchmarks demonstrate superior performance compared to SOTA methods.", "questions": "Experiments\n\n1. In Table 3, EvoQuality outperforms VisualQuality-R1. The authors should provide further discussion of this observation. Specifically, do these two methods use the same training set and training configurations (e.g., sampling strategies for batch construction)? If not, the comparison may not be fair. If they do share identical settings (meaning the only difference is whether pseudo-labels or ground-truth labels are used), then the results suggest that training with pseudo-labels is actually more effective than training with true labels. The authors should explain the underlying reasons for this counterintuitive finding.\n\n2. I suggest the authors provide an analysis of how \\pi_\\theta(⋅∣c_{compare}, x_i, x_j) evolves over training iterations. Specifically, do the K estimates of relative quality become increasingly stable as training progresses, i.e., consistently yielding K_x >> K_y or K_x << K_y? This would help demonstrate that the self-evolution mechanism is functioning as intended.\n\nMethod\n\n1. Why isn’t p^\\*(x, y) defined in a probabilistic form? Consider two cases: K_x=9, K_y=1 and K_x=6, K_y=4 . In both cases, p^*(x, y)=1, yet they reflect different levels of confidence that image x is of higher quality than image y.\n\n2. A key advantage of opinion-unaware methods is their ability to eliminate reliance on MOS, which requires substantial human and financial resources to collect. This enables scaling up training with more (and more diverse) images. However, this advantage is not clearly demonstrated in the paper, especially when comparing the training scale with that of VisualQuality-R1. Moreover, the proposed method requires a relatively large value of K, which contradicts the goal of scalable training: as the training data volume grows, the computational cost of repeatedly invoking the large vision-language model becomes prohibitively high.\n\n3. In lines 226-227, it appears that batches are constructed based on image pairs. Why is that? As shown in Eq. 3, the reward for an image is computed using all images paired with it in the set P. In other words, for a sampled pair (x_i, x_j), both x_i and x_j are processed independently. If so, why not simply sample individual images, i.e., a batch {x_m} (m=0,1,\\ldots,M−1), instead of pre-forming pairs?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "1. The paper is clearly written and easy to follow.\n2. The self-evolving framework offers some useful insights for opinion-unaware IQA.\n3. The experimental results demonstrate good performance.", "weaknesses": "My main concern about this paper is its novelty. In my view, the work essentially builds upon VisualQuality-R1. Specifically, it replaces the ground-truth relative quality labels for image pairs (derived from MOS) with pseudo-labels generated by the VLM itself. The overall framework does not differ fundamentally from VisualQuality-R1. Moreover, the idea of generating ranking pseudo-labels—whether for image pairs or even image lists—and then training an IQA model using those pseudo-labels is also not new, e.g., see \"Gu et al., No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking, AAAI 2019.\"", "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": "XBDhCKVPSN", "reviewer": "Reviewer_myjA", "summary": "This work introduces EvoQuality, a novel framework that enables a VLM to autonomously refine its quality perception capabilities without any ground-truth labels. EvoQuality generates pseudo-labels by performing pairwise majority voting on the VLM's own outputs to establish a consensus on relative quality. EvoQuality achieves performance that is competitive with, or even surpasses, state-of-the-art supervised VLM-based IQA models.", "questions": "Please see Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. EvoQuality does not need any human-annotated quality scores to regress the quality scores, which is the most interesting point, though the voting method is quite simple and intuitive.\n2. EvoQuality takes advantage of both the pre-trained VLM's ability (ie, ranking a pair of images) and the IQA theory (ie, fidelity loss), providing a new way to utilize VLMs in the IQA field.\n3. In out-of-distribution datasets, EvoQuality achieves competitive or even better performance than supervised VLM-based IQA models, though in-distribution performance is missing.", "weaknesses": "1. The in-distribution performance is missing. In Table 3, the authors should also report the PLCC and SRCC results in the KONIQ dataset. Understandably, the performance will be lower than other methods, since EvoQuality does not use GT scores. However, reporting all results honestly is necessary.\n2. Multi-dataset co-training experiments are missing. Q-Align and DeQA-Score both perform multi-dataset co-training experiments. Adding such experiments will make this work more solid.\n3. The performance is not consistent with previous literature. The performance of training on KONIQ, then evaluating on other datasets, has been reported by several previous works, like Q-Align, DeQA-Score, and Q-Insight. All three works report similar, even almost the same results. However, the results in this work are not consistent with previous ones.\n4. An important ablation is missing. The authors could utilize VLMs to rank image pairs, then calculate quality scores from all pair-wise comparison results. Then, the authors could use these scores to train previous IQA methods. With this experiment, we can fully understand whether the main performance gain of EvoQuality comes from (1) ranking accuracy of pretrained VLMs or (2) GRPO ranking training. (1) is the main contribution of this work, while (2) is the contribution of VisualQuality-R1.", "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": "e5gMgd7rc6", "reviewer": "Reviewer_d853", "summary": "This paper proposes the EvoQuality framework, which enables vision-language models (VLMs) to autonomously improve their image quality assessment (IQA) capabilities without access to ground-truth labels. The method consists of a two-stage iterative loop (Section 3): an offline stage that generates pseudo-ranking labels via pairwise majority voting on the VLM’s own outputs (Section 3.1, Eq. 1), and an online stage that constructs fidelity rewards from these pseudo-labels and optimizes the model using GRPO (Section 3.2, Eqs. 3-5). Experimental results (Table 2) show that EvoQuality increases the weighted average PLCC of the base model Qwen2.5-VL-7B by 31.8%, and surpasses supervised SOTA models on 5 out of 7 IQA benchmarks (Table 3, Fig. 1b).", "questions": "1. Table 4 shows that after the second iteration, the PLCC on KONIQ drops from 0.840 to 0.835 and on AGIQA from 0.839 to 0.831, which directly contradicts the discussion in Section 4.2 on “Progressive refinement with more iterations.” How do the authors explain this degradation? Does this suggest that introducing synthetic distortions in the second round (the 10 distortion types and 5 severity levels mentioned in Section 4.1) compromises the model’s generalization to real distortions?\n\n2. The method relies on the quality of the baseline VLM’s voting consensus (Eq. 1). When the K votes for certain image pairs are nearly tied (e.g., 16:16), is the resulting pseudo-label `p*(x, y) = 0.5` reliable? Should the paper consider setting a confidence threshold to filter out low-confidence pseudo-labels?\n\n3. In the design of Section 3, the offline stage uses a comparison-style prompt `c_compare`, whereas the online stage uses a score-estimation prompt `c_score` and converts scores to pairwise probabilities via the Thurstone model (Eq. 2). Why not train with direct pairwise comparisons in the online stage to maintain consistency with the offline stage? Does this switch in prompt types introduce additional uncertainty?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The core innovation lies in successfully transferring the principle of self-consistency from discrete reasoning tasks to the continuous perceptual task of IQA (Section 1, paragraph 2). By adopting a pairwise voting and ranking paradigm (Section 3.1), the method circumvents reliance on costly human annotations.  \n- The experimental design is rigorous and comprehensive, with zero-shot evaluation across eight diverse benchmarks (Section 4.1), spanning real distortions, synthetic distortions, and AI-generated distortions.  \n- The ablation study in Table 4 clearly demonstrates the advantage of the ranking-based method (EvoQuality) over the direct regression variant (EvoEstimate), particularly in its sustained improvement on synthetic distortion datasets. The ablation on the number of candidate responses K (Fig. 3) further validates the necessity of the majority voting mechanism; performance is most stable at K=32.", "weaknesses": "- There is an internal inconsistency regarding the central claim of “progressive improvement.” Table 2 reports that the weighted average PLCC increases from 27.4% in Round 1 to 31.8% in Round 2, yet the detailed ablations in Table 4 show that EvoQuality exhibits performance degradation or stagnation on several datasets, e.g., KONIQ (0.840 → 0.835), AGIQA (0.839 → 0.831), and LIVEW (0.847 → 0.847), with consistent gains observed primarily on synthetic distortion datasets (KADID, PIPAL, TID2013, CSIQ). This contradiction is neither explained nor discussed in the main text.  \n- The fundamental assumption, that the consensus produced by voting over the baseline VLM’s outputs constitutes sufficiently reliable pseudo-labels (Section 3.1), lacks theoretical or empirical support. The paper does not analyze whether, when the baseline model is of low quality or produces inconsistent judgments for certain image pairs, the method might amplify errors rather than improve performance.", "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": "0ulDfeXshk", "reviewer": "Reviewer_siBb", "summary": "This paper introduces a self-supervised framework that enables a VLM to improve its IQA capabilities without human-annotated data autonomously. The method called EvoQuality uses a two-stage process: first, the VLM generates pseudo-labels by performing pairwise majority voting on its own outputs to determine relative image quality. These pseudo-rankings are then used as a fidelity reward to guide the model's evolution through reinforcement learning. This iterative self-improvement loop progressively refines the VLM's perceptual abilities. Experiments demonstrate that EvoQuality substantially enhances the base model's zero-shot performance, and surpasses state-of-the-art supervised IQA models.", "questions": "Please see the weakness.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThis work represents the first application of VLMs to the IQA task in a fully self-supervised manner, eliminating the need for human-annotated labels. This is a significant contribution, given the high cost and  challenges associated with collecting large-scale subjective quality data.\n2.\tThe empirical evaluation is thorough, encompassing a diverse range of benchmark IQA datasets that include authentic, synthetic, and AI-generated distortions. Furthermore, the study includes comparisons against numerous recent state-of-the-art models, providing a robust assessment of the proposed method's performance . The finding that the self-supervised EvoQuality framework can achieve performance competitive with, or even superior to, supervised VLM-based approaches is particularly noteworthy.\n3.\tThe manuscript is well-written, presenting the methodology, experiments, and results with a clear logical flow and well-defined structure.", "weaknesses": "1. The manuscript contains typographical errors that require correction. For instance, the text mentions \"EvoRank\" in the results discussion (Section 4.2), which appears to be inconsistent with the method name \"EvoQuality\" used throughout the rest of the paper. Careful proofreading is recommended to address such inconsistencies.\n\n2. The set of competing models should be expanded to include relevant unsupervised learning IQA methods, such as CONTRIQUE and Re-IQA, to provide a more comprehensive comparison. Further, the related works can be refined by employing a higher-level view by including works like AIBench: Towards trustworthy evaluation under the 45°law, and Towards Versatile Multimedia Quality Assessment for Visual Communications.\n\n3. The motivation behind using evolving-format IQA methods is not well explained. And from the experimental results, the self-evolving method achieves quite good performance results over the supervised methods, the reasons for which are not well clarified. Is the proposed method better at capturing more representative features? Or is the evolving format more suitable for the ranking task and why? The authors should have more discussion.", "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)