Reasoning as Representation: Rethinking Visual Reinforcement Learning in Image Quality Assessment
Authors: Shijie Zhao
Xuanyu Zhang
Weiqi Li
Junlin Li
Li zhang
Tianfan Xue
Jian Zhang
Venue: ICLR 2026 Oral (2026)
Keywords: Image Quality Assessment
Low Level Vision
Multimodal Large Language Model
Primary area: reinforcement learning

Abstract: Reasoning-based image quality assessment (IQA) models trained through reinforcement learning (RL) exhibit exceptional generalization, yet the underlying mechanisms and critical factors driving this capability remain underexplored in current research. Moreover, despite their superior performance, these models incur inference energy usage and latency orders of magnitude higher than their earlier counterparts, restricting their deployment in specific scenarios. Through extensive experiments, this paper verifies and elaborates that through RL training, MLLMs leverage their reasoning capability to convert redundant visual representations into compact, cross-domain aligned text representations. This conversion is precisely the source of the generalization exhibited by these reasoning-based IQA models. Building on this fundamental insight, we propose a novel algorithm, RALI, which employs contrastive learning to directly align images with these generalizable text representations learned by RL. This approach eliminates the reliance on reasoning processes and even obviates the need to load an LLM. For the quality scoring task, this framework achieves generalization performance comparable to reasoning-based models while requiring less than 5% of their model parameters and inference time.

Published as a conference paper at ICLR 2026
REASONING AS REPRESENTATION: RETHINKING VISUAL REINFORCEMENT LEARNING IN IMAGE QUALITY ASSESSMENT
Shijie Zhao Xuanyu Zhang 121 Weiqi Junlin
Junlin Li', Li Zhang',
Tianfan Xue". Jian Zhang"
BByteDance Inc., Peekngg University, 'The Chinese University of Hong Kong
ABSTRACT
Reasoning-based image quality assessment (IQA) models trained through rein- forcement learning (RI.) exhibit exceptional generalization, yet the underlying mechanisms and critical factors driving this capability remain underexplored in current research. Moreover, despite their superior performance, these models incur inference energy usage and latency orders of magnitude higher than their earlier counterparts, restricting their deployment in specific scenarios. Through extensive experiments, this paper verifies and elaborates that through RL training, MLLMs leverage their reasoning capability to convert redundant visual represen- tations into compact, cross-domain aligned text representations. This conversion is precisely the source of the generalization exhibited by these reasoning based IQA models. Building on this fundamental insight, we propose a novel algo- rithm, RALI, which employs contrastive learning to directly align images with these generalizable text representations learned by RL. This approach eliminates the reliance on reasoning processes and even obviates the need to load LLM during inference. For the quality scoring task, this framework achieves general- ization performance comparable to reasoning-basee models while requiring less than 5% of their model parameters and inference time. Code is available at RALI
INTRODUCTION
Image Quality Assessment (IQA) is fundamental
task in the field of computer vision, with applica- tion scenarios covering two key dimensions. In nat-
0.78
RALI (Ours)
Q-Align
Q-Insight
ural scenarios, supports photography selection and
0.76
DeQA-Scoce
video platform quality monitoring (Sheikh) 2005 Lin et al. 2019 Fang et al., 2020 Wu et al.] 2024b): In the field of generative algorithms, IQA serves as a core reward signal in the Reinforcement Learning from Human Feedback (RLHF) framework (Rom- bach et al. 2022 Dhariwal & Nichol, 2021 Wang al. 2025 He et 2024), which crucial for the training process of generative image and video mod- els. Its performance directly affects the convergence efficiency and the effect of reinforcement learning strategies.
0.74
CLIP-IQA
0.72
Parzmeters
0.70
B
Qeci-SFT
C2Scorv
3.5 B
0.66
0.78
PLCC
Figure 1: Performance comparison among IQA methods in PLCC/SRCC and param- eter numbers. RALI uses only about 4% of Q-Insight's (Li while achieving comparable accuracy.
With the development of multimodal large language models (MLLMs), series of innovative methods have emerged in the IQA field. Q-Align (Wu et al.) 2024b) and DeQA (You et al.) (2025) enable MLLMs to directly output image quality scores through supervised fine-tuning (SFT). Descriptive algorithms such as DepictQA (You et al. (024b) focus on the text representation of image quality. Recently, studies represented by Q-Insight (Li et al. 2025] Zhang et al. 2025a) and VisualQuality-Rl (Liu et al. 2025c have introduced visual reinforcement
(2025)) parameters
These authors contributed equally this work.

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Published as a conference paper at ICLR 2026
learning (RL) into IQA tasks by outputting quality reasoning text during reasoning and scores af- terward. Their generalization in image quality prediction is significantly superior to previous SFT methods. Despite their strong performance, two challenges remain. First, the principle behind their generalization improvement lacks systematic analysis. Notably, while studies have explored RI. generalization in other fields (Chu et al., 2025] Liu al.) 2202cc] Pan et al.) 2025; Xu et al.) 2025), the unique complexity of visual characteristies and the subjectivity of quality evaluation in IQA tasks render direct transfer of these findings difficult. Second, stepwise reasoning incurs high la- tency and loading overhead, limiting deployment in online RL, mobile, and real-time settings. This raises two critical questions: How is generalization related to reasoning in IQA, and is it essential? To address the above questions, this paper focuses on the source of generalization of RL-based IQA models (e.g., Q-Insight (Li et al. 2025)).
Turning to the first question- How is generalization related to reasoning in IQA? Generalization has been a topic of discussion for IQA tasks, as individual datasets are typically scall-scale and there is pronounced domain gap between them due to their varying distributions in image quality and label annotations (You et al. 2025). Thus, using high-dimensional visual representations to predict scores tends to lead to overfitting. However, experimental verification reveals key finding. For reasoning- baasoning-based models like Q-Insight, the dependence in their scoring process has changed almost entirely. Instead of relying on lengthy visual tokens, it now depends on concise and compact quality reasoning text tokens. 'he core mechanism is that RL methods (e.g., GRPO (Shao et al.) 2024 Guo et al. 2025 enable MLLM acquire a dimensionality reduction strategy: reasoning. which manifests itself as mapping input images to quality reasoning text to form IQA representa- tions. Specifically, previous MLLMs typically predict image quality through visual representations (more than 1000 tokens), whereas reasoning- peasoning-based models rely primarily on the textual represen- tations (less than 100 tokens), resulting in a compression of more than 10 times. Furthermore, we further demonstrate that the text representations can mitigate domain discrepancies. Meanwhile, the reasoning process itself, that is, the conversion of images to quality reasoning text, exhibits weak correlation with specific datasets and can maintain stable alignment across different domains. To- gether, these factors explain the generalization of reasoning- based IQA models. We further validate the generalization of image quality reasoning text by proposing novel Reasoning-Aligned Cross- domain Training (RACT) framework. This approach addresses dataset distribution issues in image quality assessment tasks, enabling effective cross-domain training in misaligned data scenarios.
Turning to the second question: Is reasoning essential? From our prior discussion, we know that LLM reasoning maps images to quality reasoning text achieve generalization, but contrastive learning methods, such as CLIP (Radford et al. (202T)) (which maps text and images to a shared embedding space), can also accomplish this mapping without the need for multistep reasoning, po- tentially offering new pathway for generalization in IQA. Existing CLIP-based IQA methods (c.g.. CLIP-IQA (Wang et al., 2023)) have similar attempts but two flaws: shallow alignment with general text (not quality-specific) and overly simplistic text corpora (e.g., only "good/bad photo" and lack- ing complex quality dimensions). Building on prior research into Visual RL training mechanisms and reasoning processes, we have addressed these limitations and proposed Lightweight IQA (RALI) framework. RALI consists of three key steps: First, we acquire image- text-score data triplets via reinforeement learning; second, we align images with quality reason- ing text through CLIP-based contrastive learning; finally, for the textual score space, we leverage deseription- -crrpe pairs define higher-dimensionnl,, more sophisticated score mapping space. For score prediction, we directly map images to the pre-constructed image quality text space (without retaining the reasoning process) and accomplish scoring via intra-space similarity calculation. As shown in Fig. [ RALI uses only 4% of Q-Insight's parameter while achieving comparable scoring accuracy. Extensive experiments demonstrate that RALI achieves generalization on par with RL- based MILMs, outperforms all other methods, and eliminates both the reasoning process and the deployment of LLMs, reducing the inference time and the memory by more than 95%.
deasoning-Alignee
In summary, this paper shows that the generalization of reasoning IQA model is rooted in the com- pression of visual information into textual representations, and this finding is further corroborated by RACT. Additionally, we prove that an equivalent level of generalization can be realized through RALI, a framework that does not incorporate reasoning process or depend on LLMs.
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2 RELATED WORKS
Image Quality Assessment. Classical research divides IQA into full reference and no reference
settings. Full reference methods (Wang al. (200)); [heikh & Bovik (2066); Zhang et al. (20TT)
compare a distorted image with pristine reference using traditional metrics such as SSIM (Wang
ett al. )2004) as well as deep learning based metrics (Bosse et al. (2017); Cao et al. (2022) : Ding
et al. 2020 222202222) Ghildyal & Liu (2022); Prashhani et (
(2.1 No reference methods estimate perceptual quality without an explicit reference, evolving from hand-
like LPIPS (Zhang et al. (0018)
crafted natural scene statisties (Ma et al.
(2017): Mittal et al. (2012a b); MMoorthy & Bovik (2110)
2011]; Saad et al. (2012) to models that learn strong quality priors from data (Kang et al. (004))
Ke et al. (2021) : Liu et al. 2017); Pan et al. (2018); Su al. (2020); Zheng et al. (2021 Zhu al.
2020 Sun et al. (2022) Wang et al. (2023)
MLLM-Based IQA Methods. Recent work employs multi-modal large language models (MLLMs) to assess image quality. Score-based approaches such as Q-Align (Wu et al. (2024b) and DeQA- Score (You et al. (2025)) produce numerical ratings by leveraging the models' perception and world knowledge, yet they limit the intrinsic descriptive ability of MLLMs. Description-
ap-
proaches (Wu et al. (2025a 2024a); You et al. (2024b a): Wu et al. (2024c); Chen et al. (2024); Zhang et al. (2025c b) aim to deliver qualitative judgments with richer explanations and better interpretability. while relying on large volumes of textual annotations for supervised fine tuning (SFT). Very recently, visual reinforcement learning is introduced into IQA tasks (Li et al. (2025); Zhang et al.] (2025a): Wu et al. (2025b)) These RL-based IQA methods can jointly output quality reasoning text and scores, and show superior generalization ability to SFT-based methods.
REVISITING REASONING-BASER MLLMS IN IQA
3.1 PRELIMINARIES
Reinforcement Learning for Image Quality Assessment. Reinforcement learning (RL) improves the reasoning ability of large language models through feedback driven refinement (Christiano) et al. (217); y Silver et al.] (2117): Shao et al. (2024); Yang al. (2024); ; Ying al. (2024); Hui et al.
2202 : Zhang et al. (2024)). Recently DeepSeek-R RI Zero (Guo el al. (2025) ) introduces group rela- live policy optimization (GRPO) (Shao et al. (2024), ), which strengthens reasoning using rule-based rewards and avoids heavily supervised tine-tuning In the context of visual quality understanding, Q-Insight (Li et al.) (2025)) firstly integrates GRPO by using quality scores to construct rule based rewards and by training two tasks jointly, score regression and degradation perception. For each im- age and task specific question, the policy generates groups of answers with explicit reasoning, task specific evaluators compute rewards for score regression and for degradation type and level, and multi task GRPO update increases the likelihood of higher reward answers while a KL regularizer keeps the policy close to fixed reference. The generalization of Q-Insight is significantly enhanced by training that incorporates reasoning, this critical factor is further elaborated with explanations and experiments in the Appendix A In inference, Q-Insight outputs reasoning contents (between <think> and </think>) and then score results (between <answer> and </answer>), with the first being image quality reasoning text.
Reasoning (62 words): The inage appears to have good lighting, sharp fecus, and vibrant colors that make the sheep and grass stand out against the blue sky. There's no visible noise or distortion, which suggests the photo was taken under favorable conditions. The composition is well-framed, and the subjects (sheep) are clear and detailed.
Q-Insight
<onswer> rating 4.38 v/onswers
Q-Innight Ressoring
assessment image?
Reasoning (128 words): In this image, the sheap are in focus, which suggests that the comero likely set to good depth of field.. which adid to the visual appeal without any distrocting elements. However with the white of the sheep controsting nicely against blue and green grass. Overall
Present reasoring
<onswers ratinng 4,0 </onswer>
Qwen VL Reasoning Feature
Figure 2: Comparison of Reasoning Between Q-Insight and Qwen-VI. VI the IQA Task. (left) Qnsiitts'' ' reasoning was more concise than Qwen-ViL VI.' and more correlated with image quality. (right) On the KonIQ (Hosu et al. 2020 test set, CLIP features derived from InIiight's 's reasoning were more correlated with scores following t-SNE visualization. In this paper, we adopt the quality reasoning text between <think and </think> as the reasoning contents for clarity.
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3.2 REASONING MECHANISMS OF MLLMS IMAGE QUALITY SCORING.
In this paper, take Q- D-Iiigght case study for elaboration. We analyze the reasoning differences between Q-Insight and Qwen VL (Bai et al. (025)) the image quality scoring task, where the for- mer is a model fine-tuned by reinforcement learning for image quality scoring based Qwen VL,, while the latter is a general purpose MLLM. Two critical observations emerged from our analysis. First, it is observed that Q-Insight generated more concise descriptions with less unrelated informa-
tion. Second, we found that reinforcement learning training significantly improved the correlation between quality reasoning text and subjective quality scores. As shown in Figure 2 extracted the
features through CLIP (Radford et al. (202T)) from the reasoning outputs of Q-Insight and Owen VL
on the KonIQ testset and visualized these features using t-SNE (van der Maaten & Hinton (2008)
To further uncover the relationship between reasoning and scoring, we visualized its attention heatmap during the generation of score tokens. As illustrated in Figure 3 when the model outputs score tokens, 95% attention weights are assigned to the previously generated rea- soning text tokens (excluding the fixed prompt).
liiiin
rejout
Then's
plate bowe
Based on the analysis above, conclude that through reinforcement learning, the reasoning model shifted its dependency image quality scoring from visual to- kens to reasoning text tokens, and its text tokens were more concise and more relevant image quality.
1020 Image Tokens
Ressoning Tokens
5% Allention
95% Atlention
Attention heatmap during score-token generation of Q-Insight. primarily attends to reasoning text tokens instead of visual tokens (95% vs. 5%).
Figure 3:
3.3 KEY TO GENERALIZATION! COMPRESSING VISUALS INTO TEXT REPRESENTATION
Text a More Compact and Domain-bridging IQA Representation. It is well-established that comparable levels of representational capacity, more compact one exhibits better generalization (Bengio et al. (2013)), and text exhibits this trait. A 512x384 image requires approximately 1,000 tokens when using visual representations to predict the quality score, while fewer than 100 tokens suffice with text representations. Furthermore, reasoning models leveraging text for image quality prediction yield in-domain decent performance, confirming representational capability of the text.
Text representations can mitigate the do- main gap between different datasets. We conducted reinforcement learning training KonIQ (Hosu et al.] (2020)) and SPAQ (Fang al. (2020) separately and visualized the vi- sual tokens and reasoning text tokens of the two datasets during the image quality assessment process using (-SNE. Figure 4 demonstrates that the feature distributions of the datasets dis- play notable disparities in the visual representa- tional space, and training on features with this prominent domain gap is likely to reduce gen- eralizability. Conversely, this domain gap is al- leviated in the textual representational space.
KenlQ
KonlQ
SPAQ
Visual Token Feature
Reasoning Test Token Fenture
Figure 4: t-SNE visualization of visual tokens and reasoning text tokens from SPAQ and KonIQ.
IQA Reasoning a Generalizable Image-to-Text Compression. We further demonstrate that the reasoning process itself exhibits strong generalization in IQA tasks, that is, the reasoning processes learned across different datasets are comparable and reduce the risk of overfitring.
We conducted an experiment in which we performed RL fine-tuning on the KonlQ (Hosu et al. 2 and KADID (Lin et al. (2019) datasets separately, starting from a pre-trained Qwen VL model, while retaining only its LLM components, as these are responsible for the reasoning process. To isolate the effect the Visual Encoder, we then paired these LLMs with the pre-trained Qwen Visual Encoder and evaluated them across multiple datasets. As shown in Table II the two LLM models exhibited little differences on out-of-domain datasets, CSIQ (Larson & Chandler (2010)) and LiveW (Ghadiyaram & Bovik (2015)), with PL.CC difference within 0.01. However, for
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Table I: PLCC/SRCC Comparison of Q-Insight trained on KonIQ and KADID using the Qwen Visual Encoder. Out-of-domain results demonstrate that L.M and its reasoning processes trained across different datasets are highly consistent.
Composition
In-domain
Out- Out-of-domain
Visual Encoder LLM Trained on
KonlQ
KADID
CSIQ
LiveW
Qwen VL
KonlQ
0.876 / 0.844
0.752 0.749
0.832 / 0.788
0.837 0.789
Qwen VL
KADID
0.810
0.841 0.841
0.839/ 0.790
0.832 0.791
domain evaluations, the scoring results for the respective datasets were higher, resulting from a closer alignment between scoring preferences and in-domain characteristies.
Now we can answer the question: How is generalization related to reasoning in IQA? In summary, reinforcement learning enables the model to acquire highly generalizable compression from visual tokens to text tokens. With the strong representational capability and generalization of text tokens, the scoring process exhibits excellent generalization.
3.4 REASONING-ALIGNEI CROSS-DOMAIN TRAINING FRAMEWORK
To further demonstrate quality reasoning text as an excellent representation of IQA, we propose that it could offer new path align datasets with varying distributions. Dataset variation from divergent distributions is one of the key challenges of IQA. To address this, ranking-based NR-IQA models have been introduced and adopted in prior MLL.M-bsee IQA works (e.g.. Compare2Score (Zhu et al. (2024), ). DeQA (You et al. (025)). VisualQuality-RI (Wu et al. (202bb)) However, this challenge worsens in RL, as cross-dataset reward acquisition issues impede learning optimal rea- soning paths. In particular, Q-Insight shows severe convergence problem mixed datasets, while VisualQuality-RR RRI's ranking-basee training mitigates this, it still degrades with extensive mixed sam- ples-c.25 its PLCC on KonIQ decreased by 0.024 compared to standalone training. These obser- vations will be presented the subsequent experimental section.
Based on the aforementioned analysis of the reasoning model scoring scheme and its generalization, we design the Maasoning-AAlineee Cross-Domain Training (RACT) framework to enable co-training on multiple IQA datasets. First, conduct independent reinforcement learning training on each IQA dataset (single-domain RI. training). Second, we perform label alignment by leveraging the reasoning module to generate image quality reasoning text for each dataset, thereby forming unified cross-dataset labels in the form of image-text pairs. Third, we perform cross-domain SFT on the single-domain RL-trained model using aligned image-text pairs. For the schematic diagram, please refer to Figure 1 in the appendix. Given that both the reasoning process (from visual tokens to text tokens) and the description outputs are cross-domain aligned, the aforementioned training can be implemented across domains. This training process primarily aims to adapt the visual encoder to images of varying quality and scenes, enabling it to effectively convert them into visual tokens. Furthermore, we only introduce score information from single dataset during training, as multi- domain scores impair convergence.
4 REASONING-ALIGNE LIGHTWEIGHT IQA FRAMEWORK
We have revealed that reasoning is the key to generalization, but is reasoning essential? To an- swer this question, we design a Reasoning-Alignan
Lightweight IQA framework, dubbed RALI. It aligns the reasoning description text produced by visual RL with the Visual Encoder, enabling it achieve performance close RL-based IQA methods while offering strong advantages in speed and memory usage. Specifically, as illustrated in Figure 5 our approach follows the basic pipeline "Visual token Text token Score". and consists of three steps: contrastive alignment, feature compression, and scoring definition.
Contrastive Alignment. First, we use a pre-trained reasoning-basee IQA model Q-Insight to gen- erate reasoning texts on its training set C such as (Hosu et al. 2020). Concretely, we encourage the scoring model to assign quality scores and extract the reasoning text from <think> and </think>, forming image-text-score triplets (I, s.. Notably, for the same input image, we use different seeds to enrich the diversity of the generated quality reasoning text. We then finetune a CLIP (Rad- ford et al. 2021) vision encoder with an image- text text contrastive learning loss (Radford et al. 2021 so that it aligns with the underlying quality reasoning text space. To be noted, we freeze the text encoder and only train the image encoder during the process.
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Score: 3.875
Score: 3,875
RL-based IQA Model
Score Mapping
Quality Description
Blucked
Text Encoder
Score
Mapping
Lange I Language Model
PCA Reduction
PCA Redaction
Contrastive
Training Part
Quality-Aligned Vision Encouler
Quality-Aligned Vision Encodeer
Vision Encoder
Vision Encodor
Visual Tioken
Text Tokes
Basi Score
QualttyAlignccl Tokes
RL-besed Model
Contrastive Alignment
Festure Compression
(d) Scoring Definition
Figure 5: Illustration of the proposed crasoning-aligned lightweight IQA (RALI) framework. (a) presents the components and functions of the RL-based IQA model. (b)-dd) jointly constitute our lightweight RALI scheme, including contrastive learning with quality reasoning text, feature compression, and score definition. The model's inference pipeline identical (d).
Feature Compression. With the finetuned vision encoder Eolign we convert the I images in the
training set C into L embeddings embeddings can fit the feature space well, they may harm out-of-distribution generalization (Bengio et al., 2013), so we compress the visual tokens and reduce the visual space. We first apply the principal component analysis (PCA) to further reduce the embeddings from RD - IR here M 512, producing the compressed embeddings E and projection matrix U E IR DxM This process further reduces dimensionality and filters out
IR here is set to 768. Although high-dimensional visual
irrelevant information from raw features. Then, further reduce the number of embeddings and ensure that the retained embeddings correspond to a relatively dispersed score distribution, we partition the score range [1,5] into N buckets and define In as the index set of samples whose scores fall into the n-th bucket, i.e., In {m 8m. bucket n),, where Sm denotes the ground-truth score of sample m. For each bucket n E fI,..., N), perform k-means with kn clusters indexed by j E {1,..., k
(n)
|| Mnn 11]
j argmin,'e(1....e
E Inj
Pnj
Σ
(n) my
Em
Σ
mmj
(n) Jng Σ MET,
my
(n)
8m
Σ
mET.
MET.
where the vector Em
RR
denotes the compressed embedding of sample m, the binary assignment
variable Tm) E 1) denotes whether E,,, is assigned to cluster in bucket determined by the indicator [[.] and the nearest- rule. The cluster centroid in bucket n, cluster j. is Maji E R computed as the mean of assigned embeddings, and its representative score fnjz mean their scores. ll denotes the Euclidean norm. Finally, our bucketed k-means yields a compact feature space with K embeddings and scores aggregated over all buckets (14, f where K = Σ kn and we flatten (n.j) to global index for simplicity, here K 250, N 240.
Scoring Definition. After obtaining the compact representation space, we further finetune these basic vectors μ4, and their scores f; using image-scon score pairs {I to better match human scoring preference. Given an input image I, we use the aligned encoder Entign to compute its image embed-
ding and project it to M via the PCA matrix U. Then, we compute cosine similarities with the K basis vectors, and normalize them via softmax function to obtain weights w;, obtaining the final score as weighted sum the K basic scores.
exp(cos < U Eatign (I), p4 >) W; Σ5= exp(cos UT Eatign (I), μ2 >)
K [
f
w; ft. 1=1
(3)

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During training, we initialize with the PCA and bucketed k-means results and continue to finetune them by fitting the predicted scores f to the score labels After end-to-end learning, the basis vec- tors align better with human preferences and yield more accurate scoring results. During inference, we only need store the fixed vectors μ, and fi inference reduces simple dot products with them, incurring minimal computational overhead.
EXPERIMENTS
5.1
EXPERIMENTAL SETUP
Datasets and Metrics. Following the setup of Q-Insight (Li et al. 2025), we evaluate on broad
suite of IQA datasets spanning four groups: (a) in-the-wild collections KonIQ (Hosu et al. 2020),
SPAQ Fang al., 2020). and LIVE- Wild (Ghadiyaram & Bovik 2013); (b) synthetic distor-
tions KADID (Lin et al. 2019). CSIQ (Larson & Chandler 2010) and TID2013 (Ponomarenko
ct al. 2015); (c) pooel-processed distortions-PIPAA (GU et al. 2020); and (d) Al-generated im-
ages AGIQA (Li et al. (023). Mean Opinion Scores (MOS) from all datasets are rescaled to the
interval [1.5]. For score regression, we report performance using the Pearson Linear Correlation Correlation Coefficient (SRCC).
Coefficient (PLCC) and the Spearman
Implementation Details. For RALI, we choose CLIP-VTTLLARGE-PPTCIII 4-336 as our vision en- 4-336
coder. The learning rate is set to 1 x 10 10TS in contrastive alignment and set to x 10 in scoring fitting. We use PCA reduce the dimension of the original feature space from 768 to 512 duce some noise and interference. The number of basic vectors and buckets are respectively set to 250 and 240. For RACT, we use -WEN-2.5-VL-7B-B INSTRUCC (Bai al., 2025) as our baseline models. Within GRPO, we sample N = 8 candidates per update and apply a KL regularizer with coefficient 3 1 X 10 The auxiliary losses use weights α; = 0.25 and (22 = 0.75. We use AdamW (Lshchilov & Hutter, 2017) with initial learning rate of 1 x 10 that decays linearly to 10 over training. The model is trained for 10 epochs with batch size of 128, and the full run completes in approximately one day on 8 NVIDIA H20 GPUs.
5.2 RESULTS OF SCORE REGRESSION
Results of Single-dataset Training with RALI To evaluate the effectiveness of our proposed RALI, we compare method with handcrafted methods such as NIQE (Mittal et al. 2012b); non-MLLM deep-learning methods including NIMA Talebi & Milanfar (201)), MUSIQ (Ke et al.
2021 CLIP-IQA+ (Wang al., 2023), and ManIQA ( Yangg et al. 2022 and recent MLsMebasedd
methods such as C2Score (Zhu et al. 2024). Q-Align [Wu et al. 2024b, DeQA-Scort (You et al.]
2025), supervised fine-tuned Qwen Bai
2025), and Q-Insight (Li et al.) 2025). For a fair comparison, all methods (except handcrafted ones) are trained on the KonIQ dataset.
Our comparison results are reported in Table 2 It can be observed that our method achieves compet- itive results to the SOTA model Q-Insight on PLCC and SRCC. Mcanwhile, compared to QnInitth (7B parameters), we only use about 4% of the parameters and significantly shorten the running time and storage overhead. Compared to the SOTA none MLLM-based method CLIP-IQA+, our method also surpasses by 0.056 and 0.059 on PL.CC and SRCC respectively. This fully demonstrates the efficiency of our freee scheme and the accuracy of score fitting.
Results of Multi-dataset Co-training with RACT. RACT's results for cross-doman training are presented in Table 3 with comparisons to four baseline methods: VisualQuality-Ri -R11 (Liu et al.) 2025ch, Q-Align et 2024b), DeQA (You et al., 2025), and Q-Insight (Li et al. (2025). Among these, Q-Align and DeQA SFT-based MLLMs, while InIighht and VisualQualit--R are RL- based MLLMs. All algorithms are co-trained on the KonIQ, SPAQ. KADID, and PIPAL datasets, and test results are split into in-domain and out-of-domain groups for detailed comparison.
On in-domain datasets, SFT-based algorithms show a clear advantage over RL-based counterparts. Specifically, Insight exhibits poor in-domain fitting due to the lack of cross-domain alignment; On out-oo--domin datasets, RL optimized via RACT achieves the highest performance across all out-of-domain datasets.
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Table 2: PLCC / SRCC comparison on the single-domain score regression tasks between RALI and other competitive IQA methods. All methods except handcrafted ones are trained on the KonIQ dataset. The best and second-best results of each test setting are highlighted in bold red and underlined blue.
Category
Methods
KonlQ
SPAQ
KADID
PIPAL
LiveW
AGIQA
CSIQ
AVG.
NIQE
0.533
0.679
0.468
0.195
0.493
0.560
0.718
0.521
Mittll al., 26
/0.530
/0.664
/0.405
/0.161
/0.449
/0.533
/0.628
/0.481
Hundcrafted
BRISQUE
0.225
0.490
0.429
0.267
0.361
0.541
0.740
0.436
Mittal et al. 2012a
/0.226
/0,406
70.356
/0.232
/0,313
70,497
70,556
/0,369
C2Scorre
0.923
0.867
0,500
0.354
0.786
0.777
0.735
0.706
Zhu et al. [0244]
/0.910
/0,860
/0.453
/0.342
/0,772
/0,671
/0.705
/0,673
Q-Align
0.941
0.886
0.674
0.403
0.853
0.772
0.671
0.705
ct 2024b
70.940
/0.887
/0.684
/0.419
/0.860
70.735
/0.737
/0,752
DeQA
0.953
0.895
0.694
0.472
0.892
0.809
0.787
0.786
MLLM-based
You et al. 2025
/0.941
/0,896
/0.687
/0.478
/0,879
/0.729
70.744
/0,765
VisualQuality-B
0,923
0.891
0.712
0,441
0,874
0.822
0.712
0.768
[Wu ct al. 2025b
/0,908
/0,892
/0.711
/0.438
/0,849
/0.767
/0.662
/0,747
Q-Inight
0.933
0.907
0.742
0.486
0.893
0.811
0.870
0.806
cL al. 2025
/0.916
/0,905
/0.736
/0.474
/0.865
/0.764
/0.824
/0,783
MUSIQ
0.924
0.868
0.575
0.431
0.789
0.722
0.771
0,726
[Kc ct al. 2021
/0.929
/0,863
/0.556
/0.431
/0,830
/0,630
/0.710
/0,707
ManIQA
0.895
0.864
0.654
0,419
0.805
0.685
0.719
0,720
Non-MLLM Deep-leaming
Yang al. 2022
/0.849
/0.768
/0.499
/0.457
/0.849
/0.723
/0.623
/0.681
CLIP-IQA+
0.909
0.866
0.653
0.427
0.832
0.736
0.772
0,742
(Wang al.
2023
/0.834
/0.758
/0.465
/0.452
/0.832
/0.636
/0.627
/0.658
LIQE
0.901
0.860
0.720
0.480
0.842
0.739
0.782
0.761
Zhang et al. 2023
/0.895
/0.862
/0.735
/0.501
/0.865
/0.697
/0.808
/0.766
RALI
0.939
0.897
0.723
0.527
0.896
0.779
0.828
0.798
(Ours)
/0.922
/0.897
10.725
/0.528
/0.876
/0.715
/0,788
/0,779
Table 3: PLCC/ SRCC comparison on the cross-domain score regression tasks between RACT and other MLLMs based IQA methods, All methods are trained on the KonIQ, SPAQ, KADID and PIPAL datasets.
In-domain
Out-of-domain
Methods
KonTQ
SPAQ
KADID
PIPAI.
AVG.
LiveW
AGIQA
CSIQ
IID13
AVG.
Q-Align
0.926
0.917
0.950
0.702
0.874
0.853
0.765
0.838
0.811
0.817
eWu.et al. 2024b
70.932
70,920
/0.954
70.671
70,869
/0.845
/0.722
/0.789
/0.795
/0.788
DeQA
0.958
0.932
0.963
0.724
0.894
0.877
0.770
0.863
0.828
0.835
You al. 2025
/0.946
/0,929
70,961
/0.690
70,882
/0,857
/0.735
/0,807
/0.796
/0.799
VisualQuality-R
0.899
0.918
0.918
0.603
0.834
0.852
0.812
0.859
0.799
0.831
Liu ct al. 2025c
/0.881
/0,914
/0,920
/0.588
/0.826
/0.834
/0.753
/0.772
/0.764
/0.781
Q-Insight
0.899
0,913
0.757
0.579
0.787
0.867
0.805
0.768
0.743
0.796
al. 2025
/0.871
/0,907
/0.765
/0.559
/0.776
/0,830
/0.757
/0,720
/0.651
/0.740
RACT
0,928
0,922
0.919
0.642
0.853
0.881
0.813
0.892
0.844
0.858
(Ours)
/0.907
/0.918
/0.916
/0.626
/0.842
/0.846
/0.763
/0.838
/0.817
/0.816
5.3
ABLATION STUDIES
Ablation on RALI'S Key Components. To assess the effectiveness of each component in RALI, we conduct ablation studies on the key components including Contrastive Alignment, PCA Re- duction, Bucketed K-Means, Seed Augmentation, and Scoring Definition. The average PLCC and SRCC results, computed in line with single-domain experimental settings, are presented in Table When contrastive alignment omitted and the original CLIP weights are used directly, we observe significant degradation in scoring performance. This is because CLIP primarily attends to high- level semantic space and does not adequately interpret the quality reasoning text. When removing PCA reduction and directly use CLIP's native 768-D features, we observe a slight drop in scoring performance, since PCA effectively removes noise in feature fitting and improves generalization. Replacing bucketed k-means with standard k-means leads to substantial degradation in RALI's IQA performance, as the resulting cluster-base scores are overly concentrated and fail to cover the full score range. Without using multiple seeds augment quality reasoning text, the CLIP model
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Published as a conference paper at ICLR 2026
Table 4: Ablation studies on the key components of RALL. It can be observed that alignment descriptions and scoring definition based on basis vectors with scores significantly enhance the performance of our method.
Case
Casc
Casc
Case
5
Case
Contrastive Alignment
PCA Reduction
v
Bucketed K-Means
Seed Augmentation
x
/
Scoring Definition
/
X
AVG. PLCC
0.748
0.792
0.785
0.793
0,743
0.798
AVG. SRCC
0.727
0.772
0.766
0.773
0.723
0.779
Table 5: Ablation labels and training modules in cross-domain SFT. Scores yield no out-of- domain gain, and fine-tuning only the Visual Encoder (VE as in the table) suffices for comparable performance, cross domain reasoning is aligned.
Label
Train Module
In-domain
Out-of-domain
Text
Score
VE
LLM
KonIQ
KADID
CSIQ
AGIQA
V
0.926 0.900
0.92470.920
0.878 / 0.843
0,804
/
2
0.927 / 0.905
0.920 / 0.915
0.883 / 0.820
0,808 0.751
0.928 0.907
0.919 0.916
0.881 0.846
0.813/0.763
insufficiently aligned and struggles converge well. Finally, even without defining and fitting scores on the dimension-reduced basic vectors, the model already surpasses CLIP-IQA+, which demonstrates the effectiveness of our contrastive alignment; adding the score definition further im- proves the accuracy of score prediction.
Ablation on Labels and Training Modules in RACT. We incorporated scores into cross-domain SFT and found they only benefit in-domain performance, with no out-of-domain improvement. The reason is that cross-dataset annotations carry annotator biases-tex. text retains objective quality, but scores incorporate subjective aspects. Training scores makes the model overfit these variable biases, hence no out-of-domain gain. As discussed earlier, single- single-datasct-larmee reasoning is gen- eralizable, so cross-dataset training only needs to tune the Visual Encoder for cross-domain image inputs. We conducted comparative experiments, and the results are shown in Table 5 Training the Visual Encoder alone and joint training with the LLM yielded comparable performance, which is consistent with our earlier conclusion. However, we observed slower convergence when training the Visual Encoder only.
O-inrihht
2808
RALI
Quinsigtt Memmry
5.4 EFFICIENCY STUDIES OF RALI
RAL Nemory
-25
20
As discussed earlier, reasoning MLLMs consume substan- tial GPU memory due to large parameters and require mul- tistep reasoning, further raising inference costs. RALI of- fers strong generalization with drastically lower deployment and inference costs than MLLMs. The tests the NVIDIA A 100 (80GB), as shown in Figure 6 reveal the marked effi- ciency advantage of RALI over Q-Insight: batch size 16, it consumes only 14.7% of Q-Insight's 's memory and 3.4% of its inference time.
Inference (st
1963
Memory FOB
156B
15
20
3.26
4,850
-5
2008
2964
64
Figure 6: Efficiency comparison be- tween Q-Insight and RALI.
CONCLUSION
In this paper, we revisit reasoning MLLMs in image quality assessment and find their generalization stems from compressing visual information into descriptive text a compact, domain domain-bridgig rep- resentation. Building on this, we pursue two complementary directions. To start with, we leverage this textual representation to develop the Reasoning- Aligned Cross-domain Training (RACT) frame- work, addressing divergent data distributions: it delivers SOTA mut-of-distribution performance on mixed training. Going a step further, we propose the Reasoning-Alignee Lightweight IQA (RAL.) framework, which matches reasoning MLLMs in image-to-text mapping by integrating contrastive learning (image-text alignment), PCA (dimensionality reduction). and bucketed K-means (label-text conversion) to delineate quality scoring space. achieves comparable performance with only 0.3B parameters and explicit reasoning. Overall, work reveals how reasoning MLLMs generalize in IQA, provides efficient high-performance solutions, and informs future IQA model design.

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Published as a conference paper at ICLR 2026
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Zicheng Zhang, Tengchuan Kou, Shushi Wang. Chunyi Li, Wei Sun, Wei Wang, Xiaoyu Li, Zongyu Wang, Xuezhi Cao, Xiongkuo Min, et al. Q-eval-100k: Evaluating visual quality and alignment level for text-to-vision content. arXiv preprint arXiv:2503.02357, 2025c.
Zicheng Zhang, Haoning Wu, Ziheng Jia, Weisi Lin, and Guangtao Zhai Teaching Imms for image quality scoring and interpreting. arXiv preprint arXiv:2503.09197 2025d.
Heliang Zheng, Huan Yang. Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo. Learning conditional knowledge distillation for regraded--rfference image quality assessment. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10242-10251, 2021.
Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi. Metaiqa: Deep meta- learning for no-reference image quality assessment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14152- 2020.
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. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2024.
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APPENDIX
A
THE RELATIONSHIP BETWEEN REASONING AND GENERALIZATION IN Q-INSIGHT
More Experiments. To further investigate the relationship between reasoning and generalizability, have conducted experiment where Qwen2.5-VI. is trained on KonIQ via GRPO without in- corporating the reasoning process in Table(A.] Through comparisons, we observe that while GRPO training yields modest performance improvement over SFT, Q-Insight with the reasoning capabil- ity disabled exhibits a significant performance drop, especially in the OOD testing dataset. This validates the correlation between reasoning and generalization in this task.
More Discussions, Beyond experiments, we further elaborate on the relationship between reason- ing and generalization. The Q-Insight paper already validated this via comparative experiments and analyses of Qwen-SFT and a suite of SFT models. Q-Insight's core motivation and insight is to in- spire the large model to "reason deeply and develop insightful perspectives on image quality metrics during scoring" rather than merely teaching it "how to score images." Removing the reasoning process reduces training to mere score-based constraints, causing the model to forfeit this critical advantage.
Furthermore, many previous works have discussed the relationship between reasoning and gener- alization. For instance, Visual-RFT (Liu et al., (022c) also notes that "The reasoning process is key to the model's self-learning and improvement during reinforcement fine-tuning." The essence of Visual-RFF lies in teaching the model "how to think" rather than "what answer to memorize," which constitutes its core competitive advantage over traditional SFT.
In addition, during training Q-Iniight, observed that excessively short reasoning lengths leads to performance drop. Other studics have also discussed related phenomena; for example, (Jin et al.] 2024 mentions that insufficient reasoning length can impair LLM performance. Conversely, remov- ing reasoning entirely represents the most extreme case and would result in a significant decline in generalization.
Table A.1: PLCC/ SRCC comparison of different training strategies. Q-Insight with the reasoning capability disabled exhibits a significant performance drop.
Method
KoalQ
SPAQ
KADID
PIPAL
LieW
AGIQA
CSIQ
AVG.
Qwen- SPT
866
0.74608875
0.668/0663
044330.442
0.7340.728
0,81300739
0.674/0.650
0.7320.
Qwen GRPO (wlo Reasoning)
0.9218.901
0.904/0901
0.701/0.898
0.59004522
0.87808852
0.787/0728
0.728/0.577
0.768/0744
Qwen GRPO Ressoingg Q-InightO
0.93300.116
0,907/0.005
0,742/0.736
0/86004444
0.39308865
0.811/0.74
0.87000.824
0.806007733
B LIMITATIONS AND BROADER IMPACTS
Limitations. Although our lightweight RALI achieves strong results, its performance ceiling is still constrained by the representational and reasoning capacity of the underlying CLIP vision en- coder. In future work, will explore stronger CLIP variants (e.g., SigLIP (Zhai al.) 2023). Meanwhile, following Q- Insight, our experiments primarily target natural- imaree IQA; however, we believe our reasoning-aligee
lightweight approach, together with cross- ross-domain training, can be readily extended to video and AIGC quality assessment.
Broader Impacts. Our analysis of reasoning- MILMMS is not confined to image quality assess- ment, it readily extends to broader vision language tasks. In particular, our examination of attention mechanisms in reasoning MLLMs, our exploration of compact textual representation spaces, and our considerations for mitigating cross-domain bias offer actionable insights for future research. More- over, the proposed reasoning- aligned lightweight IQA framework provides a general and convenient pathway convert reasoning- based evaluators into reasoning-free free ones. This efficient paradigm not only facilitates on-device deployment, but also substantially streamlines the use of reward models in post-training pipelines, such as (Liu et al. 2025b a)
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RL
Score: 75/100
RL
Score: 1.58/5
Score
Seore
Reasoning
Reasoning
Descriptions
SFT
MLLM
MILLM
Score
MLLM
Deurppoos
(u) Single Domain RL Training
(b) Label alignment
(c) Cross Domain SFT
Figure A.I: Illustrations of the proposed deasoning-Aligned Cross-Domain Training Framework (RACT). (a) Single-domain RL: We train an MLLM each IQA dataset to produce reasoning and scores. (b) Label alignment: We use the reasoning module to convert images into quality reasoning text, forming unified image-text labels across datasets. (c) Cross-domain SFT: We finetune the RL- trained model with the aligned image-text pairs to adapt the visual encoder across domains; only one dataset's scores are used stabilize convergence.
C MORE DETAILS ABOUT REASONING-ALIGNED CROSS-DOMAIN TRAINING FRAMEWORK.
Framework and More Discussion. A detailed pipeline illustrated in Figure A. the model trained in this manner can be conceptualized as follows: train the sion module using annotation information from multiple datasets, while the description-to-score prediction module is trained solely on annotations from single dataset. In our ablation stud- ies, we further note that incorporating scores from multiple datasets into SFT results in degraded performance. Our interpretation of this phenomenon lies in the dual-component nature of dataset annotations: they encompass both objective image quality and annotator group bias. Through re- inforcement learning, the model has already acquired objective image quality reasoning text-ones capabilities. What remains, however, is annotator
conver-
that possess generalizability and Nomain-bridging group bias, which exhibits substantial gap across different dataset domains. A more intuitive illus- tration of this is: if groups A, B, and C are mutually unrelated, fitting group C's preferences using only group A's biases yields no improvement compared fitting them using the combined biases of groups A and B.
Advantages of RACT Agianst Naive Multi-dataset RL Training. To enable clearer comparison between RACT and Q-Insight, we conducted two additional experiments: specifically, mixed train- ing experiments both methods on the KonlQ and SPAQ datasets. These experimental results are consolidated in single table to facilitate comparison.
As shown in Table A.2 1) Compared to Q-Iniight trained on a single dataset, mixed training leads to performance degradation of Insight, and this degradation becomes more severe as the number of mixed datasets increases. 2) In the same training settings, RACT exhibits better convergence and generalization. Moreover, more datasets are included, the additional gains of RACT become more significant.
Table A.2: PLCC / SRCC comparison between Insigttt and RACT across different training set-
tings and datasets.
Training Datasets
Method
KentQ
SPAQ
KADID
PIPAL
Live
AGIQA
CSIQ
T1D
AVG.
Konlg
Q-Insight
0.9330.916
0,907700905 905
0.74240.36
0,6560.674
8.89
0.110 564
0.8700.K24
0,76900656
0,79840.00 767
KunlQ,
Insight
0198819933
0.92800924
0.71997111
0.8964.482
0.89608611
0.16/99547
0.81697754
0,72800638
0,78900754
RACT
9:92919909
0.92309988
0.75281742
0.477A4467
0.75008477
011491757
0.89000
0.75220662
0.N02N0 768
KonlQ, SPAO, KADD, PIPAL.
Q-Insipl
0.9990.67
099150907
(.5766.59)
0.86770
0.766
0.74N0665
RACT
0,925/1.907
0.92210918
0.919/99166
0,642M0.26
0.88180846
081300963
0.892/0.838
0.84408817
0.85540.829
The Impact of Different Score-labeled Datasets on RACT's Performance. In the experiments presented in Table 3 only the numerical scores from KonlQ were used to train RACT RACT-speciffe specifically, cross-domain training was conducted based on I-sigttt pre-trained on KonIQ. We conducted cross- domain training with RACT using Q-Inithht pre-trained SPAQ and performed comparative anal-
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yses, leading to the conclusion that RACT is not sensitive the dataset from which training numeric scores are sourced.
Table A.3: PLCC/ SRCC comparison when training RACT with numeric scores from different datasets.
Scores From
KonlQ
SPAQ
KADID
PIPAL.
LaveW
AGIQA
CSIQ
TTD
AVG.
KoalQ
0.928/0.907
0.922/0.918
0.919/0.916
0.612/0.226
0.881/0.846
0.813/0,763
0.892/0838 0.897008444
0.844V0.817
0.858/0.116
SPAQ
8.91700892
0.925/0.920
0.92180.919
0.63006613
0.88000.888
0.80660.761
0.841/0.812
0.852/0.836
Designed Prompts. The prompts designed for each task in RACT are detailed in Tab. A.4 For the single dataset RL training, the input includes a task-specific prompt and the image to be rated, with the Mean Opinion Score (MOS) serving as the ground-truth. For the multi-datasets SFT, the input includes a task-specific prompt and the image to be described, with the quality reasoning text serving as the ground-truth.
Table A.4: Prompts for RACT.
System Prompt for RL Training: A conversation between User and Assistant. The user asks question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within < think >< /think > and < answer
ansswr tags, respectively, i.e., think > reasoning process here < /think >< answer > answer here < /answer >
Prompt for Score Regression Task: What is your overall rating the quality of this picture? The rating should be float between and 5, rounded to two decimal places, with representing very poor quality and 5 representing excellent quality. Return the final answer in JSON format with the following keys: "rating": The score.
Prompts for Quality Description Task: What is your overall assessment of the quality of this picture?
D MORE ABLATION STUDIES
Ablation on the Hyperparameters of the Proposed RALI. To further demonstrate the rationality of our hyperparameter choices, we sweep the PCA dimension, the number of basis vectors, and the
number of buckets. Results are reported in Table A.5 We find that when the PCA reduction is too low or the number of basic vectors is too small (e.g.. 256-D/ / 100), both these settings (case (1)-(6) and 512-D (case (8)) can achieve roughly 0.745 PLCC without score definition. However, due to substantial information loss, the dimension-reduced bases cannot adequately fit the feature space, and thus after applying score definition they fail to reach higher performance ceiling. When the number of buckets is too small, the basis scores become overly concentrated and cannot effectively cover the full score range. Conversely, with a very high basis dimensionality (c.g.. 700-D), the model tends to perform better in in-domain scenarios but exhibits reduced generalization out of domain. Moreover, excessively high dimensionality and large number of bases increase the optimization difficulty of RALI. Thus, we choose case (8) as our final solution.
E RELATION TO MODEL LIGHTWEIGHTING
An alternative strategy for model acceleration involves directly applying conventional model lightweighting techniques to MLLMs. How do these methods compare to our RALI approach? There are distinct differences: Firstly, standard lightweighted algorithms typically remain archi- tecturally homologous to their original counterparts, whereas RALI differs fundamentally from MLLM-based algorithms in terms of architectural design. Secondly, RALI achieves an extreme lightweighting ratio of up to 95% while maintaining performance comparable to that of MLLMs, an efficiency perforranee balance that standard lightweight models do not achieve. To validate this, conducted controlled experiment: Qwen- VL model using the KonIQ dataset, with results presented in Table A.6, Experimental find- ings reveal that when the parameter count is reduced by approximately 50%, the MILMM exhibits
performed reinforeement learning training the 3B
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Table A.5: Ablation study the hyperparameter selection of RALII
Score Definition
PCA
Basic Vectors
Bucket Bins
Case
PLCC
SRCC
Dimension
X
256
100
90
0.748
0.720
V
256
100
90
0.785
0.764
3
X
256
250
240
0.747
0.718
V
256
250
240
0.783
0.762
5
X
512
100
90
0.745
0.717
V
512
100
90
0.783
0.762
X
512
250
240
0.743
0.723
V
512
250
240
0.798
0.779
X
700
250
240
0.745
0.719
700
250
240
0.787
0.767
a significant performance drop, and 3B Q-Insight cannot skip the reasoning. This confirms that lightweighting Q-Insight cannot match the performance of RALI.
Table A.6: PLCC SRCC comparison single-domain trained on the KonIQ dataset. Q-Insight (3B) shows significantly lower performance than RALI (Ours) after 50% parameter reduction.
regression tasks. All methods
Methods
KonlQ
SPAQ
KADID
PIPAL
LiveW
AGIQA
CSIQ
AVG.
Q-Insight (3B)
0.907
0.897
0.704
0.445
0.824
0.831
0.826
0.776
(Li et al. 2025
/0.887
/0.892
/0.699
10.452
/0.788
/0.758
/0.798
/0.753
RALI
0.939
0.897
0.723
0.527
0.896
0.779
0.828
0.798
(Ours)
/0,922
/0.897
/0.725
/0.528
/0,876
/0.715
/0.788
/0.779
F VISUALIZATION
We further present visualization comparisons of reasoning traces and scores between RACT and VisualQuality-RI Figures A.2 and A.3 As shown in Figures A.2 and A.3] our method produces concise reasoning that is better aligned with image quality, and its predicted scores are consistently more accurate than those VisualQuality-R..
G LLM USAGE STATEMENT
We used large language model (LLM) only for minor grammar and phrasing polishes. All technical content, including ideas, experiments, analyses, and discussions, was entirely created by the authors.
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What is your overall rating the quality of this picture? The rating should be a float between and 5, rounded to two decimal places, with representing very poor quality and 5 representing excellent quality. Return the final answer in JSON format with the following keys: \"rating\": The score.
GT Score: 3.95
<think> The image appears clear and well-lit, capturing the details of the White House, the statue, and the tulip garden effectively. The colors are vibrant, especially the red tulips, which stand out against the green grass and the white building. There are visible distortions or blurs that would affect the overall quality. The composition is balanced, with the statue and the White House as focal points. </think>
VisualQualit--R1
<answer> 4.70 < /answer>>
The image appears clear with good lighting and sharp details. The colors are vibrant, especially the red tulips, which stand out against the green grass and the white building. There's s no visible noise or distortion that would lower the quality significantly. The composition well-framed, capturing the White House, the statue, and the flowerbeds ef effectively.
<think>
</think>
RACT (Ours)
<answer> 3.92 </answer>
Figure A.2: Visualization comparison between our proposed RACT and visualquality-R (Wu et al.
2025b) on the CSIQ dataset.
What is your overall rating the quality of this picture? The rating should be a float between and 5, rounded two decimal places, with representing very poor quality and 5 representing excellent quality. Return the final answer in JSON format with the following keys: \"rating\" The score.
GT Score: 3.59
<think> The image appears clear and well-lit, with good visibility the architectural details such as the stone wall texture, the wooden door, and the window with shutters. The colors are natural and there's have been edited or filtered, which slightly affects its natural appearance but does not significantly degrade the overall quality. </think> <answer> 3.80 //answer>
noticeable noise distortion. However, the image seems to
VisualQuality-R1
<think> The image appears clear, with good contrast and sharpness that allows for the details of the stone wall, door, and window to discernible. There's ss no evident motion blur or significant distortion affecting the image quality. However, there seems to be some noise slight graininess present, which might slightly reduce the perceived quality. </think>
<answer> 3,50 </answer>
RACT (Ours)
Figure A.3: Visualization comparison between proposed RACT and visualqualit--RI (Wu al. 2025b) on the TID2013 dataset.
19


Reviews:
[{"review_id": "8g2Rw97Lmf", "reviewer": "Reviewer_xqw8", "summary": "This paper argues that RL-trained reasoning-style IQA models, such as the Q-Insight, don’t generalize better because they’re “better models,” but because RL teaches them to compress rich visual features into short textual descriptions of quality. This text space is said to be both compact and more cross-domain aligned, and that is claimed to be the source of generalization of RL-based IQA models. Based on that, they introduce RACT, Reasoning-Aligned Cross-domain Training, which first does per-dataset RL to obtain quality descriptions, aligns those descriptions across datasets, and then does cross-domain SFT using those aligned image–text pairs. They claimed that the proposed RACT enables RL-style OOD robustness without instability when mixing datasets. Furthermore, they propose RALI, which discards step-by-step reasoning at inference, aligns a CLIP-style vision encoder to those descriptions via contrastive learning, compresses features via PCA + bucketed k-means, and then predicts scores via similarity to a learned bank of basis vectors.", "questions": "1. As claimed in the paper, RALI is trained only on KonIQ, and its cross-dataset performance is reported (indicated by Table 2). Are the reasoning texts generated only from KonIQ images?\n\n2. Continuing the previous question, if so, then RALI’s OOD performance hinges on how well Q-Insight’s KonIQ-derived descriptions generalize to distortions and semantic content in SPAQ, AGIQA, CSIQ, etc. But the paper argues earlier that cross-domain alignment of reasoning is what generalizes, which implies we might get even better generalization if we harvest reasoning texts from multiple datasets. However, RALI is not shown in that stronger setting. How would the authors explain this?\n\n3. Which single dataset’s numeric scores are actually used during cross-domain SFT in Table 3’s RACT row? KonIQ only? SPAQ only? How sensitive is RACT to that choice?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper has a clear attempt to answer new open questions, which is more valuable and challenging than reporting new performance numbers. It tries to explain why RL-style IQA models outperform SFT baselines in cross-dataset generalization, which is timely and valuable regarding the internal mechanism.\n\n2. The proposed RACT and RALI pipelines are conceptually neat and practically actionable. Their effectiveness is verified on diversified IQA datasets. RACT is empirically strong OOD, where it outperforms both SFT-style and RL-style baselines in average PLCC/SRCC, with only the quality descriptions as a unifying label space across different datasets.", "weaknesses": "1. The paper repeatedly asserts that RL teaches the model to compress visual tokens into textual reasoning, and that “this conversion is precisely the source of the generalization.” Although interesting, all supporting evidence is correlational, not causal. For example, fig. 3 shows attention mass shifting from visual tokens to reasoning tokens when outputting the score. But this only proves where the model is looking at inference time after reasoning text exists, not that the reasoning step itself is required to learn generalizable features. Provide causal ablations that can quantitatively prove this claim, or this would be fatal for this paper.\n\n2. In section 4, the authors explicitly claim that the proposed RALI eliminates the reliance on reasoning processes and even obviates the need to load an LLM. However, it is also stated that RALI first uses a pre-trained RL IQA model to generate reasoning text between \\<think\\> and \\<think\\> and scores for each training image. Therefore, although RALI is cheap at inference, it cannot be trained or updated without first running an RL reasoning model like Q-Insight to produce the textual descriptions that define that space. Can I interpret the contribution in this way: this paper is actually proposing distillation (or compression) of an RL reasoning model into a CLIP-like scorer, instead of removing reasoning entirely.\n\n3. The empirical evidence for RACT’s cross-domain convergence fix is not complete enough in the main part. As claimed in Sec. 3.4, RL on mixed datasets shows a severe convergence problem; the VisualQuality-R1’s ranking-based RL mitigates this but still drops when jointly trained, and multi-dataset reward construction is unstable. However, the paper doesn't quantitatively show the alleged instability/failure modes of naive multi-dataset RL training that motivate RACT. Since RACT is the core contribution, this point is very important but missing.\n\n4. RALI intentionally discards reasoning at inference. That’s great for speed, but some downstream pipelines want textual justifications. The paper doesn’t quantify how often RALI’s score disagrees with the RL model’s score on failure cases where the RL model also gave a plausible, human-auditable explanation.\n\n5. Some of the parameters look tricky and potentially fragile. The score range [1,5] is divided into N=240 buckets, then bucketed k-means with kn clusters per bucket yields 250 basis vectors, and why? Is that engineered around KonIQ’s and Q-Insight’s output distribution?\n\n6. The paper uses “reasoning,” “quality descriptions,” and “text tokens” almost interchangeably. But some of the provided examples look less like multi-step logical reasoning and more like structured perceptual justifications. Calling that “reasoning” may oversell what RL actually learned. I think clearer terminology would help.\n\nIn summary, I highly value the motivation and the ambition to answer open questions of the IQA field in this paper. However, I think current experiments and argumentation are not enough to support both the motivation and contribution. Several core claims are currently stronger than the presented evidence supports, especially the causal claim that “reasoning-as-compression is the source of generalization,” and the claim that RALI “removes” reasoning despite relying on RL-generated reasoning text for supervision.", "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": "EUMg67rZYH", "reviewer": "Reviewer_fd2c", "summary": "In this paper, the authors raise two critical questions: how generalization relates to reasoning in image quality assessment (IQA), and whether it is essential, motivated by the observation that IQA scores are largely determined by reasoning text tokens rather than visual tokens.\nTo answer the first question, the authors propose a reasoning-aligned cross-domain training (RACT) framework to address divergent data distributions by leveraging textual reasoning representations derived from unified cross-dataset labels.\nTo answer the second question, they propose a reasoning-aligned lightweight IQA (RALI) framework to learn multimodal large language models' (MLLMs) reasoning-based IQA capability by fine-tuning a small vision encoder which is further generalized via feature compression.\nIn experiments, RALI achieved comparable IQA-scoring performance using only 4% of the parameters and runtime of Q-Insight.", "questions": "No questions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "In Section 3.2, the paper presents a very interesting observation: the reasoning-based MLLM IQA model, Q-Insight, primarily references text tokens when generating the image quality score token, while hardly referencing visual tokens.\n- Building on this observation, the paper's development—proposing a solution to the stated problem—is highly logical and well-grounded.\n\nIn particular, by effectively leveraging the relatively domain-invariant nature of text representations compared to visual representations, the method secures generalization performance on the IQA task with a simple approach.\n- Both proposed methods, RACT (label alignment and cross-domain SFT) and RALI (feature compression), incorporate solutions for cross-dataset adaptation.\n- The approach is validated by the experimental results (Table 2 and Table 3).\n\nMoreover, compared to the prior art Q-Insight, it has a significant advantage for practical deployment by maintaining performance while reducing parameters and runtime to only 4% of Q-Insight.", "weaknesses": "Regarding L176: 95% of the attention weights are assigned to the previously generated reasoning text tokens.\n- The 95% shown in Figure 3 is for a single example. Reporting the corresponding statistic over the entire KonIQ test set would enhance credibility.\n- Performing the same analysis as in Figure 3 on Qwen-VL would help generalize the claim about the reasoning mechanisms of MLLMs in image quality scoring.\n\nFigure 4 may be somewhat self-evident.\n- Because KonIQ and SPAQ contain different images, the visual tokens differ substantially, meanwhile, the text space used to describe IQA is relatively constrained (the terms and expressions for IQA are limited within the broader text corpus).\n- As in the right plot of Figure 2, adding scores to Figure 4 to show clustering by IQA score regardless of dataset would be more informative.\n\nIn the multi-dataset co-training results (Table 3), to better highlight the effect of RACT, it would be preferable to use a combination of four datasets that are very different in nature as described at L335 (e.g., KonIQ, KADID, PIPAL, and AGIQA, rather than KonIQ, SPAQ, KADID, and PIPAL) if possible.", "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": "hzYRm24GXW", "reviewer": "Reviewer_Wmn3", "summary": "The paper investigates the generalization capabilities of reasoning-based IQA models, particularly those trained with reinforcement learning. It argues that these models achieve superior generalization by converting redundant visual representations into compact, cross-domain aligned text representations (quality descriptions). Based on this, the authors propose RALI, a new framework that uses contrastive learning to align images directly with these generalizable text representations, bypassing the need for explicit reasoning and LLMs during inference. The paper demonstrates that RALI achieves comparable performance to reasoning-based models with significantly reduced computational cost.  The authors also introduce RACT, a Reasoning-Aligned Cross-domain Training framework to improve co-training on multiple IQA datasets.", "questions": "- Have you considered applying RALI to other vision-language tasks, such as image captioning or visual question answering?\n- What are the limitations of RALI? What types of images or distortions does it struggle with?\n- You mention the reduced computational cost of RALI. Can you provide a more detailed breakdown of the computational cost of each step in the RALI pipeline, compared to Q-Insight?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) The core idea of linking generalization in IQA to the compression of visual information into text is insightful.\n2) The proposed RALI framework is well-designed and technically sound. The combination of contrastive learning, PCA, and bucketed k-means is effective.\n3) The experimental results are compelling and clearly demonstrate the effectiveness of RALI. \n4) The paper is generally well-written and easy to follow. The figures and tables are well-designed and informative.", "weaknesses": "- While the paper convincingly demonstrates the importance of text representations, it sometimes overstates the case by implying that reasoning itself is not important. Reasoning, even if implicit, is still involved in the process of generating the quality descriptions used to train RALI. The paper needs to acknowledge this nuance.\n- RALI relies on Q-Insight to generate the initial image-text-score triplets. This raises concerns about the potential for bias in the generated data. It would be helpful to explore the sensitivity of RALI to the quality of the Q-Insight model.\n- The improvement of RACT is not significant enough. The authors should provide more solid evidence to support the effectiveness of RACT.\n- Can you clarify the role of reasoning in your framework? While RALI doesn't explicitly perform reasoning during inference, the quality descriptions it relies on are generated by a reasoning-based model. How does the quality of the initial reasoning affect RALI's 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": "eIvfGTSO8v", "reviewer": "Reviewer_6GPX", "summary": "This paper investigates the generalization mechanism of reasoning-based reinforcement learning (RL) models for image quality assessment (IQA). The authors argue that the strong generalization of RL-trained multimodal large language models (MLLMs) stems from their ability to compress redundant visual representations into compact, cross-domain text representations through reasoning. Based on this insight, the paper introduces two novel frameworks:\n\n1. RACT (Reasoning-Aligned Cross-domain Training): aligns multiple IQA datasets via reasoning-generated text to improve cross-domain generalization.\n\n2. RALI (Reasoning-Aligned Lightweight IQA): leverages contrastive learning to directly align images with reasoning text embeddings, achieving comparable performance to reasoning-based MLLMs with only 4% of their parameters.", "questions": "Please see Weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper investigates the generalization of reinforcement learning–based IQA methods. Although I believe the experimental setup is not entirely sufficient, it represents a frontier study in this field and carries significant research value.\n\n2. The RALI framework is quite interesting. Essentially, it distills a lightweight CLIP model using the reasoning text data generated by a reasoning-based model, and it further establishes a mapping between reasoning texts and quality scores. This design effectively avoids the problem of inconsistent score scales across multiple datasets during joint training. Meanwhile, textual information, compared with direct supervision using single numerical scores, may enhance the model’s generalization ability. From this perspective, I think the approach is somewhat similar to that of Q-Align, except that Q-Align directly maps scores to a few fixed textual tokens. I think this work provides an insightful strategy for multi-dataset joint training.", "weaknesses": "１.\tLine 70–71: The authors state that “Instead of relying on lengthy visual tokens, it now depends on concise and compact reasoning (quality description) text tokens.” I disagree with this statement because, for both reasoning and non-reasoning models, the inputs are images (i.e., visual tokens). The reasoning model additionally generates a group of text tokens during output, derived from score-level supervision through RL. Therefore, it is unclear why the authors claim that reasoning-based models “depend on text tokens.” In my view, those text tokens are outputs rather than inputs.\n\n\n２.\tLine 74–76: The authors state that “previous MLLMs typically predict image quality through visual representations (more than 1000 tokens), whereas reasoning-based models do so via textual representations (less than 100 tokens), resulting in a compression of more than 10 times.” Similar to my comment in Question 1, I do not understand why reasoning-based models are described as not relying on visual tokens. These models still take images as input and thus require visual tokens to generate reasoning text. They cannot directly output reasoning tokens without first processing the visual input. Therefore, I disagree with this statement as currently written.\n\n\n３.\tFigure 2: The caption refers to subfigures (a) and (b), but the figure itself does not contain such labels. Please revise the figure or caption for consistency.\n\n\n４.\tSection 3.2: The comparison between Q-Insight and Qwen-VL appears to be unfair, as Qwen-VL has not been trained on any image quality assessment (IQA) dataset and lacks the capability for quality scoring. It is therefore expected that its reasoning outputs are longer and less relevant than those of Q-Insight, which the t-SNE visualization also reflects. A more appropriate comparison would involve a version of Qwen-VL that has been SFT-trained on the same IQA dataset. Furthermore, I do not fully agree with the conclusion that “through reinforcement learning, the reasoning model shifted its dependency on image quality scoring from visual tokens to reasoning text tokens.” As mentioned in Questions 1 and 2, reasoning text tokens and scores are both outputs of the reasoning model. The text tokens serve to explain why the model assigns a particular score, but the claimed “dependency shift from visual tokens to reasoning text tokens” is conceptually confusing, since these belong to two different stages of the processing pipeline.\n\n\n５.\tFigure 4: The comparison between visual tokens and reasoning text tokens appears to be unfair. To ensure a fair comparison, the visual model should also be fine-tuned on the same dataset, and the visual features used for comparison should be extracted from the last layer of that model to ensure sensitivity to image quality.\n\n\n６.\tTable 1: A compression experiment is missing—specifically, results for Qwen-VL trained via supervised fine-tuning should be added to show its in-domain and out-of-domain performance for comparison.\n\n\n７.\tLine 239: The authors mention that “its PLCC on KonIQ decreased by 0.024 compared to standalone training.” Please provide a reference for this number. Moreover, according to Tables 2 and 3, the proposed model also suffers a 0.011 drop, which should be discussed for completeness.\n\n\n８.\tLine 293–295: The statement “Although high-dimensional visual embeddings can fit the feature space well, they may harm out-of-distribution generalization, so we compress the visual tokens and reduce the visual space.” Please clarify how this conclusion is derived.\n\n\n９.\tTable 2: The order of methods and corresponding references is inconsistent across entries; please standardize the formatting.\n\n10.Tables 2 and 3: An important baseline, LIQE, is missing. LIQE also uses CLIP as its backbone and performs multi-dataset training; it should be included for a fair comparison.", "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 (Oral)