MiCo: Multi-image Contrast for Reinforcement Visual Reasoning
Authors: Xi Chen
Mingkang Zhu
Shaoteng Liu
Xiaoyang Wu
Xiaogang Xu
Yu Liu
Xiang Bai
Hengshuang Zhao
Venue: NeurIPS 2025 poster ()
Keywords: Visual Reasoning
Chain-of-Thought
LLM
VLM
MLLM
Primary area: deep_learning

Abstract: This work explores enabling Chain-of-Thought (CoT) reasoning to link visual cues across multiple images. A straightforward solution is to adapt rule-based reinforcement learning for Vision-Language Models (VLMs). However, such methods typically rely on manually curated question-answer pairs, which can be particularly challenging when dealing with fine-grained visual details and complex logic across images. Inspired by self-supervised visual representation learning, we observe that images contain inherent constraints that can serve as supervision. Based on this insight, we construct image triplets comprising two augmented views of the same image and a third, similar but distinct image. During training, the model is prompted to generate a reasoning process to compare these images (i.e., determine same or different). Then we optimize the model with rule-based reinforcement learning. Due to the high visual similarity and the presence of augmentations, the model must attend to subtle visual cues and perform logical reasoning to succeed. Experimental results demonstrate that, although trained solely on visual comparison tasks, the learned reasoning ability generalizes effectively to a wide range of questions. Without relying on any human-annotated question-answer pairs, our method achieves significant improvements on multi-image reasoning benchmarks and shows strong performance on general vision tasks.

MiCo: Multi-image Contrast for Reinforcement Visual Reasoning
Xi Chen'
Mingkang Zhu
Shaoteng Liu
Xiaoyang Wu
Xiaogang Xu
Yu Liu
Xiang Bai
Hengshuang Zhao
HKKU
Tongyi Lab, Alibaba Group
CCUHK
*HUST
Abstract
This work explores enabling Chain-of-Thought (CoT) reasoning to link visual cues across multiple images. A straightforward solution is to adapt rule-based reinforcement learning for Vision-Language Models (VLMs). However, such methods typically rely on manually curated question-answer pairs, which can be particularly challenging when dealing with fine-grained visual details and complex logic across images. Inspired by self-supervised visual representation learning, observe that images contain inherent constraints that can serve supervision. Based on this insight, we construct image triplets comprising two augmented views of the same image and third, similar but distinct image. During training, the model prompted to generate a reasoning process to compare these images (i.e., determine same or different). Then optimize the model with rule- based reinforcement learning. Due to the high visual similarity and the presence of augmentations, the model must attend subtle visual changes and perform logical reasoning to succeed. Experiments show that, although trained solely on visual comparison tasks, the learned reasoning ability generalizes effectively a wide range of questions. Without relying on any Human-annotatee question-anae answer pairs, our method achieves significant improvements on multi-image reasoning benchmarks and shows strong performance on general vision tasks.
1 Introduction
Making visual analysis with multiple images is crucial in many real-world applications. For example, understand actions through sequential images videos, gain 3D awareness by recognizing multiview images, and analyze events by observing differences between states, etc. Although Vision Language Models (VLMs) [2, 8, 19, 15, demonstrate promising capabilities in understanding single images, we find them struggle to link visual cues across multiple images.
Multi-image understanding requires not only identifying fine- fine-grained visual cues but also performing logical reasoning to uncover correspondences and differences among images. Recently, reasoning in language models [12, 16, 31, 27] has been significantly improved through the of Chain-of- Thought (CoT) prompting, especially when combined with
reinforcement learning [28]. Therefore, straightforward idea improve multi-iman understanding is to extend this reinforcement learning paradigm to the visual domain. However, GRPO [28] requires constructing question-ansse answer pairs with standard answers compute rewards, which is particularly challenging for tasks involving fine-grained visual details and complex logic across images.
Instead of focusing on constructing QA pairs, we explore how to incentivize VLMSs to perform multi-image reasoning with minimal data preparation cost. Modern VLMs already possess strong perceptual and multimodal capabilities. Meanwhile, recent advances in RL-based single image
*Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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reasoning [34, 23, 5] suggest that reasoning ability can be effectively acquired with limited data. However, most of these methods still rely on task-specific supervision, such as hand-crafted QA pairs. To reduce the reliance on manual annotations, we draw inspiration from self-supervise visual representation learning [7, 14, 13, 3], where images are used as their own source of supervision. Contrastive learning methods [14, 7], for instance, learn discriminative representations by pulling together features from different views of the same image and pushing them away from those of different images. Guided by this principle, we exploit inherent constraints in images supervision signal for reward calculation, and present a novel method, MiCo (Multiple image Contrast).
Specifically, we construct training triplets consisting of two augmentations of the same image and a third, different but similar image with its own augmentation. We prompt the VLM to output the thinking process and make comparisons among these images to answer same/different. Multiple trajectories are sampled per example, and reinforcement learning is applied using advantages computed from the correctness of the final answer. A key aspect of our approach is the design of challenging image comparisons. If negative samples are too distinct, the reasoning is trivial. We address this by sampling frames from the same video or using image editing datasets to find similar images, ensuring subtle differences that require careful visual inspection and reasoning. Beyond this contrastive framework, we also introduce Augmented GRPO, a training strategy that samples trajectories using weak augmentations and optimizes them under stronger augmentations. This design allows high-quality CoTs to generalize more difficult images.
Although the model is trained solely on the image comparison task, the learned ability to link visual cues across multiple images generalizes to a wider scope of scenarios. For example, the model can predict plausible future actions by analyzing visual changes across frames, distinguish object identities by comparing fine-grained appearance details, or detect subtle camera movement in scene transformations. Moreover, the contrastive learning process encourages attention to fine-grained details, which also benefits certain single-image understanding tasks like fine-grained layout/attribute understanding. Experimental results show that MiCo achieves strong performance for multi-image understanding [40, 32, 10], and also brings improvements on general vision tasks [11, 6, 39].
2 Related Work
Vision language model reasoning. Recent studies show that ceassoninn-capaall LLMs [16, 12, 31, 27] can be effectively guided to generate long CoT [36] reasoning processes through reinforcement learning, leading to significant progress on tasks involving complex logic. Building on these advances, a surge of recent works [5, 29, 26, 25] has extended CoT reasoning into the lanion-language domain. For example, MM-Eureka [23] expands training data coverage across domains and refines RL training strategies. NoisyRollout [20] introduces image augmentations to enrich the exploration space for policy optimization. LVAA-Thinking [4] provides a detailed analysis of supervised fine-tuning and RL for visual reasoning, along with curated dataset for related tasks. ThinkLite [34] further improves data efficiency via sample selection with Monte Carlo Tree Search. While these methods rely heavily on curated training data generated by existing models or human annotations, our work explores an alternative: leveraging inherent constraints within visual data to naturally elicit reasoning ability-without explicit question-answe answer supervision.
Multi-image understanding. Understanding multiple images is crucial in real-world scenarios that require comparing object states, tracking actions, or recognizing objects across views. Recent large VLMs [2, 18, 17, 1, 15] have begun to support multi-image inputs natively. LLaVA-Interleave [19] extends LLa [19] to process interleaved multimodal inputs. VISC [41] introduces focus-centric data to enhance visual reasoning. Meanwhile, numerous benchmarks [32, 10, 24, 21, 44, 37] have been proposed to evaluate multi-image understanding from various angles. Despite these developments, recent evaluations [40, 46] highlight persistent limitations: VLMs often fail to link fine-grained visual cues across images, such identifying the same object under different views or detecting subtle state changes for predictive reasoning. Our work addresses this gap by incentivizing the model to compare the fine details across images and make logical analysis.
3 Method
3.1 Pilot Study for Multi-image Understanding
We begin with a pilot study to assess how well current VLMS understand multiple images. As shown in Fig. 1, we present examples that highlight the capabilities of several otate-of-the-art VLMs, Qwen2.5-
2

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Exomple
Question How many people there these images?
Question:
How does the comere move?
Qwent
V
Ieharnlji The camero oppeors move diong path that..
Intereli There are person visill suross these insges. V
The right and slighlly fanward...
Example 3
Exampic
Quastions How many distinct Toys can idintiff these inages?
Questions Are there city differences besides the comera movement?
Initervt. "hhre one cietrect varble there
grean LEG0 X
5ve. IIn the first inoge, the gorillss are riside fheir cape. while in the
6PT-4o There are has duriect LESO car toys in the images Lemborghini Sids PVP 37 Appoore Imagen
secord tmage. they
their oage.. X
Wustong Shebyy 61500 Appeans Image
GPT4 Yes, there are several diffeennces. The cage lebeled "SORILLA" ns
lboled 'IOON and contains has lloss. X
Figure 1: Challenges for multi-image understanding. While recent works support multiple images as input, most of them focus on scenarios where each image can be interpreted independently (e.g.. Example 1), which remains relatively easy for current state-of-the-an VLMs. However, many real- world tasks (e, g., Example 2-4) require models to compare subtle visual differences, align visual cues across images, and reason about object perrepondencee-cappiie
that current VLMs still struggle with. We gather Example from MuirBench [32], Example from VLM2- -ench [40]. Example 2,4 from real world samples.
VL [33]. InternVL [8], and GPT-4o [15]. While many recent models and benchmarks [24, 32, 44] support multi-image or video inputs, they primarily focus scenarios like Example 1, where each image can be understood in isolation. In Example I, models correctly identify a single person across images, reflecting solid basic perception. However, when we examine more complex cases in Fig. 1, we observe that VLMs often suffer from severe hallucinations. In Example 2, both models fail to infer the correct camera movement, showing weaknesses in spatial reasoning. In Example 3, VLMs cannot distinguish between different car toys, indicating difficulty with cross-image comparison. Example 4 further reveals failure in tracking semantic changes across images, with hallucinated object positions and misidentified labels. These cases highlight that current VL.Ms still lack robust visual comparison abilities essential for multi-image understanding. These examples typically require the model to explicitly link visual cues across images, analyze fine-grained differences, and reason about inter-image correspondences.
As current VLMs already possess strong abilities in single-image perception (e.g., reading fine-grained text) and demonstrate solid commonsense knowledge, evidenced by their performance standard vision benchmarks. We hypothesize that their primary limitation in mulli-image understanding lies in their inability to compare and connect visual information across images. To address this gap, we focus on enhancing the meta-cognitive ability of visual comparison, the core skill needed for effective multi-image reasoning.
3.2 Multi-image Contrast
Rather than collecting data for each specific multi-image task, we aim to improve VLMS' general capacity to analyze and reason over multiple images by targeting the core meta skill: visual comparison. Inspired by the principles of self-supervised learning, we design a lightweight and scalable framework that encourages the model to distinguish similar yet distinct images. By simulating contrastive visual situations and prompting the model to generate structured reasoning trajectories, we aim to enhance its ability to perceive -na-grai differences, establish correspondences, and perform step-by- by-epp comparisons across images.
Here, we elaborate on the pipeline of MiCo. The overall framework consists of the following main steps. First, we identify and construct contrastive image samples that are visually similar yet different. Then, we apply data augmentation to build informative training triplets. Finally, we leverage Augmented GRPO to evaluate a set of reasoning trajectories and optimize the VLM accordingly.
3

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Some
Different
Some
Different
Vidco data
Edhtin data
Figure 2: Demonstrations for contrastive samples. The first row shows two triplets from the video, and the second row demonstrates samples from image editing datasets. These samples are visually similar but contain subtle differences (marked with red circles), on which we apply random cropping and resizing. In each triplet, the first two images are the same, and the third image is different.
Image selection. We begin by selecting image pairs that are visually similar but exhibit subtle differences, which serve as contrastive supervision signals. We denote such pair as (La Ib). where In and In are distinct images sharing high structural similarity (e.g., similar layout or background), but with small detail variations.
We leverage two types of data sources that naturally fulfill this requirement. video frames and image editing datasets. For video data, we randomly sample (4a 1s) from the same video with a temporal gap of 2 seconds, and compute their Structural Similarity (SSIM) filter near-identical pairs. For image editing data, each (la, Ib) pair consists of a "before" and "after" edited image. We compute the pixel-wise Mean Squared Error (MSE) to remove significantly different pairs. These constraints ensure that the collected pairs exhibit subtle but meaningful changes.
Image augmentation. While the selected image pairs already exhibit subtle variations, directly learning to distinguish them may still lead to shortcut learning. To increase task complexity and encourage detailed reasoning, apply data augmentation to create diverse image views.
As the visualization examples provided in Fig. 2, given a source image I, we generate two augmented versions via random cropping and resizing (they do not change the content of images). For each image pair (La; In), we thus construct a contrastive triplet:
T = (Ti (I,), (2.1), Ta(b))
3.3 Augmented GRPO
Question-answer formulation. After getting the triplets that contain similar images and their augmentations. We construct QA pairs for reinforcement learning. Given image triplet, we add reasnoning prompt and user questions as follows:
Reasoning Template of MiCo
Reasoning Prompt: First output the thinking process in <think> </think> and give the final answer <answer> </answer> tags.
User Question: Regardless of the augmentation, are imagel and image2 the same? How about image2 and image3, imagel I and image3? Only return T(True) or F(False) in <answer> </answers, for example <think> </think> <answer>\FF<<answer.
To increase the diversity and balance the difficulties of questions, besides the image triplet, also construct image pairs and design the corresponding prompts for comparing two images. In addition, we use GPT-4o [15] to expand the user question of the same meaning but with various expressions.
Rollout augmentation. For each question q with augmented images T, the original GRPO samples group of outputs {or, 02, ... oG) from the old policy Te and then optimizes the policy model ma. To better leverage difficult samples that arise from strong augmentations, we sample trajectories using weakly augmented inputs T which are easier to produce valid reasoning chains. These sampled trajectories are then used to optimize the policy on stronger augmented contexts T effectively transferring reliable behavior to harder instances.

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Algorithm MiCo: Reinforcement Multi-image Reasoning
Input: Policy Ty, old policy *Bau image triplet dataset D {(41, 12, f3)}, training steps Tiaxx
group size G, clip parameter weak augment operators T strong augment operators T
2: for to max do
3: Sample triplet (11, 11) ~
4: Apply weak augmentation: (IT" I -
5:
Apply strong augmentation: (7f 12. 13)= = -T I2. T2)
6:
Construct prompts q" and q from the weak and strong augmented triplets, respectively & Rollouts from weak prompt
7:
Sample G CoT responses {o, from Tew qT))
8:
Evaluate reward R; = R(1", qt, O.) for each i L I.... Compute group baseline R = & Ei=t Rg, and advantages A, R
9:
10:
Optimize σe on the strong prompt q" using the group rollouts:
((0) = 2 Ei= min (r,A,,cip(rs, - -, + e)A.). where r; = xa(o,19") Tew q²)
11:
12:
- 0 - VeL(0)
13:
fold +- 0
14:
end for
Training objective. The training objective of Augmented GRPO could be formulated as follows.
This objective encourages the policy assign higher likelihoods responses with higher relative rewards within each group.
I(0) Elg P(Q, fo:1.-1
(Olg)
Tomi (Olg)] TT((:11) T (0s1g) )
no mo(oil
As - DxL (Toll (1)
min
A,, clip
G 1=1
Teat (0s1g)
(O; 1 Tre / q) 1, To(o; 9)
DrL (TellTref))
log
(o 4)
where and |3 are hyperparameters. Following GRPO [28], A₁ is the normalized advantage computed based rewards fri,
A F: --mmmeaarrr rr,))))))))))))
,ra))
(td((r1, 72, ... (rG))
Following DeepSeek-RI [12], we leverage the binary format reward and the accuracy reward, which considers the matching "<thinks </think> <answer> </answer>" tags, and the correctness of the final answer. For the triplet comparisons, we get for the accuracy reward only if we make correct comparisons for all three pairs.
Overall algorithm. Our MiCo could be summarized in Algorithm 1. We first construct an image triplet consisting of augmented views of the same image and third, visually similar but distinct image (with augmentations). The model is prompted to perform multi-image comparison and generate reasoning trajectories. During training, thain-of-thougee augmented views. and the policy is optimized on the strongly augmented ones using rule- based reinforcement learning. This process enables the model learn fine-graines visual reasoning in self-supervised manner.
responses are sampled from the weakly
4 Experiments
4.1 Implementation Details
Hyper-parameters. For the baseline model, we follow previous works [23, 20, 34, 4] and select
Qwen2.5-VI-7B [2]. For the training data, we use OmniEdit [35] for image editing pairs and extract video frames from Vidgen- [30]. The part of reinforcement learning follows GRPO [28], we set
5

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Table 1: Performance on VLM2-Bench [40], which evaluates the ability to compare and link fine- grained visual cues across multiple images. Without relying on any human- or model-annotated data, MiCo achieves significant improvements and sets new state-of-the-ar Reasoning- basedd models (marked with .) are evaluated using their corresponding prompting strategies.
Baxelines or Models
General
Object
Person
Overall
Mal
Trk
Cpe
Cnt
Grp
Cpr
Cnt
Gip
VID
Avg
Asrrs
Chance-Lovel
25.00
25.00
50.00
34.88
25.00
50.00
34.87
25.00
32.73
-61.44
Human Level
95.06
98.11
96.02
94.23
91.29
97.08
92.87
91.17
100.00
95.16
0.00
à VLaVooOneVissoo [7]
16.60
13.70
47.22
56.17
27.50
62.00
46.67
37.00
47.25
39.35
-55.81
< LLaVA-Video-TB [43]
18.53
12.79
54.72
62.47
28.50
62,00
66.91
25.00
59.00
45.65
-49.51
LonyVQA-7B [42]
14.29
12.98
46.53
49.47
29.00
58.00
41.56
25.00
45.00
37.10
-58.06
mPI.UG-OW12277 [38]
17.37
18.26
49.17
62.97
31.00
63.00
58.06
29.00
43.00
40.87
-54.11
Qwen2-V1--7R [2]
18.07
19.18
68.08
61.84
37.50
72.00
67.92
47.00
55.25
49.76
45.40
e InteraVL2.5-8B [8]
41.24
26.53
72.22
67.65
40.00
85.00
66.67
52.25
50.25
55.41
-39.75
Iniem2L2..5268 [8]
30.50
30.59
43.33
51.48
52.50
59,50
59.67
61.25
45.25
45.59
-49.57
Qwen2.5-VL-7B [2]
35.91
43.38
71.39
41.72
47.50
69.00
54.82
-40.34
GPT-4o [15]
37.45
39.27
74.17
80.62
57,50
50.00
90.50
47.00
66.75
60.36
-34.80
MM-Eureka-7B [23]
55.60
47.03
74.10
$2.50
$4,00
77.50
60.00
$1.00
43,50
57.24
-37.91
7o [20]
40.93
43.83
63.33
50.83
34,50
70.50
63.33
47.00
36.50
50.08
-5.00
ThinkLite- VL-7B [34]
40.45
46.58
75.56
62:50
49,50
77.50
62.50
51.00
36.50
$5.79
-39.37
VLAA-Thinker--78 [4]
47.49
65.03
72.20
61.40
55.00
71.00
57.50
51.00
47.75
58.49
-36.67
о Qwen22. VL.72..-V
43.24
42.92
66.39
50.56
36,00
62.50
55.83
39.00
36.75
48.91
46.24
MiCo-7B-CoT
57.14
67.12
81.94
56.67
58.00
65.00
57.50
62.00
44,25
61.06
-34.09
Improvement
+13.90
+24.20
+15.55
+6.11
+22.00
+2.50
+23.00
+7.50
+12.93
+12.93
format reward and accuracy reward with the weight of 1:1, respectively. Besides, we also apply KL regularization with a weight of 0.01. During training, we follow previous works [23] to skip the rollout group with all correct/false answers. During training, we use learning rate of le-6 and set the batch size of 16. For each training sample, we generate a group of 8 rollouts. We train the model for 600 iterations on 8x A 110 GPUs.
Evaluation protocols. For evaluation, we follow the default syper-parameters of Qwen2.5-VL [33] and utilize the VLMEvalKit [9]. For reasoning baselines, we adopt their official prompting formats. Minor inconsistencies in results may occur due to differences the implementation details of evaluation frameworks or answer parsing logic.
4.2 Result Analysis for Multi-image Comparison
Evaluation metrics. We first report the model performance on VLM2-Bench [40]. This benchmark mostly aligns with our intention of linking fine-grained visual cues across images. Specifically, VLM2-Bende includes three tracks: General Cue (GC), Object-centric Cue (OC), and Person-centric Cue (PC). Each track consists of subtasks with specific metrics: Mat (Matching) and Trk (Tracking) use paired T/F accuracy; Cpr (Comparison) evaluates consistency by requiring the model to correctly answer both a positive and its corresponding negative statement; Cnt (Counting) uses normalized error to measure numerical prediction accuracy; Grp (Grouping) is a multiple-choice task assessing clustering ability; and VID (Video Identity Describing) is scored based on GPT-4o evaluation of open-ended descriptions.
Result analysis. As shown in Tab. we present the comparison results on VL.22-Bench. We observe that all existing open- and closed-source models lag behind human performance by a large margin. Among them, GPT-4o [15] demonstrated clear advantages over other models. Thanks the strong generalization ability of reinforcement learning, recent reasoning VLMs [23, 20, 34, 4] have shown consistent improvements when built upon Qwen2.5-VL-7B [2]. We report the performance of Mi in the final block. Trained with contrastive triplets, MiCo effectively learns the core ability to compare images, achieving substantial gains across multiple tasks and obtaining the best average performance overall. Notably, our 7B model even outperforms GPT-4o. However, we find that CoT reasoning does not benefit all sub-tasks equally. Specifically, for tasks involving human faces (person track), CoT- based models offer limited or even negative gains compared to no-CoT counterparts. We hypothesize that human identity representations, such as facial nuances, are difficult to verbalize, thus limiting the benefit of language-bast based reasoning. In contrast, object-level identity differences (e.g., logos,

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Table 2: Ablation studies on key configurations. We conduct experiments on VLM2-Bench [40] and report the average accuracy across the general, object, and person tracks. For each ablation, all other settings are kept consistent with our final model to ensure fair comparisons.
(a) Learning Paradigm
(b) Data Source
General
Object
Person
General
Object
Person
Qwen2.5-VL. [2]
39.64
53.53
63.44
Data' [35]
61.23
65.33
56.35
42.90
51.15
55.98
Edit Data? [45]
60.88
64.33
55.27
No-CoT RL
45.36
50.01
55.23
Video Data
60.29
64,50
55.68
CoT RL
62.13
65.53
57.18
Edit
Video
62.15
65.53
57.18
(c) Rollout Augmentation
(d) Sample Formulation
General
Object
Person
General
Object
Person
Qwen2.5-V1. [2]
39.64
53.53
63.44
Qwen2.5-VI.
39.64
53.53
63.44
(Strong, Strong)
59.41
64,00
56,98
Image Pairs
56.41
66.98
55.68
(Weak, Weak)
55.58
62.03
54.81
Image Triplets
60.64
65.33
55.81
(Weak, Strong)
62.13
65.53
57.18
Pairs + Triplets
62.13
65.53
57.18
(e) Prompt Divensity
(f) Image Augmentations
General
Object
Person
General
Ohject
Person
Qwen2.5-VL [2]
39.64
53.53
63.44
Base (Crup. Resize)
62.13
65,53
57.18
Single Prompt
55.53
64.16
$1.50
Base Flip
61.13
63.98
56.77
20 Variations
62.13
65.53
57.18
Base Rotat.
62.58
65.03
55.86
Variations
63.13
65.29
54.93
Base Color.
60.15
64.26
54.86
textures, shapes) are more readily describable, allowing CoT reasoning to help reduce hallucinations and improve distinction.
4.3 Ablation Studies
We conduct series of ablation studies to validate the effectiveness of our core designs. As initial exploration of visual reasoning, we also analyze the impact of some basic configurations.
Training strategies. In Tab. 2 (a), we evaluate different training paradigms. We first apply supervised fine-tuning (SFT) on our contrastive dataset, allowing the model directly predict the final answer. We observe that this leads to minor gains on the general track, which more closely aligns with the training task. However, the ability acquired through SFT does not generalize well to more diverse reasoning tasks. We also test no-CoT reinforcement learning baseline, where the model is trained to output answers directly using GRPO [28]. Due to the absence intermediate reasoning steps, the resulting trajectories are short and behave similarly to SFT, yielding limited improvements.
Data source. In Tab. 2 (b), we compare different training data sources. Both the image editing data (OmniEdit [35]) and video-derived frames (VidGen [30]) individually support effective learning. Combining these two heterogeneous sources further enhances performance. We also validate that our framework is not tied to specific editing styles, as models trained on either OmniEdit or UltraEdit [45] generalize well, demonstrating robustness the editing domain variation.
Rollout augmentation. In this work, we leverage weak augmentations for rollout sampling, and use these high-quality answers optimize harder questions with stronger augmentations. In Tab. 2 (c), we report different combinations of augmentation in "(sampling, optimization)" process. We show that, strong augmentations are vital for contrastive learning compared with weak augmentations, and our rollout augmentation strategy gets the best performance.
Sample formulation. As discussed in Sec. 3.3, construct prompts based on either image pairs or image triplets. While we initially suspected that binary image-pai comparisons (with 50% guess probability) might result in low-quality CoTs, experiments reveal that they still contribute positively to performance. In practice, we find that combining both formats paciaats-p-irbbeeee pair--aarsee based and triplet-based leads the best results.
Other configurations. Tab. (e), (f), we explore the effects of prompt and augmentation diversity. We observe that increasing the variation of image prompts helps prevent overfitting, with performance
7

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Table 3: Quantitative results on general vision benchmarks. We report performance for wide scenarios. Multi-image benchmarks are marked bold. MiCo brings steady improvements compared with our baseline, and gets competitive results against other visual reasoning models.
MuirBench [32]
BLINK [10]
Hallusion [11]
MMStar [6] MMMU [39]
MathV [22]
MM-Eurcka-7B [23]
60.57
54.39
68.45
65.73
54.11
72.00
NoisyRollout-7BD [20]
59.61
56.07
66.66
65.66
54.55
71.60
VLAA-Thinker-7B [4]
61.00
54.81
69.08
63.60
54.44
70.80
ThinkL 7B [34]
57.62
55,81
72.97
66.80
53.55
71.89
Qwen2.5VL-7B [2]
58.43
55.54
69.50
64.06
54.11
67.10
MiCo-7B
60.53
57.23
69.61
65.60
54.77
67.90
A Improvement
+2.10
+1.69
+0.11
+1.54
+0.66
+0.80
Table Task analysis for visual reasoning. We list representative sub-tasks from MuirBench [32]
and BLINK [10] to analyze the generalization ability and limitations for MiCo.
Visual retrieval
Semantic Corr.
Spatial Rela.
Scene Under.
Forensic Det.
Relative Depih
Qwen2.5VL. VV--BB [4]
63.69
33.09
88.81
61.82
48.48
81.45
MM-Eurcka77B [23]
57.19 +
33.09 +
82.51
67.74 ++
50.00
75.80
VL.A--Thiner-771 [4]
68.88 38.83-1 +
34.53
86.71
69.89 ++
47.72
76.61
MiCo-7IB
71.23
41.72
90.20
63.97
47.72
78.22
saturating at around 50 distinct prompt templates. For image augmentations, we experimented with various techniques and ultimately selected random cropping and resizing as the default setting based on empirical performance.
4.4 Analysis on General Vision Tasks
In this section, we evaluate the generalization ability and capacity boundaries of MiCo a broader range of vision tasks. We first report quantitative results on additional benchmarks and analyze performance across more diverse task types.
Results on additional benchmarks. As shown in Tab. 4, we evaluate MiCo on MuirBench [32] and BLINK [10], both of which representative multi-image understanding benchmarks. To further assess generalization, we also include several Single-image benchmarks, including MMStar [6], MMMU [39], HallusionBench [1 I1, and Math Vista [22].
Compared to methods trained with manually curated supervision, our contrastive learning framework exhibits strong performance on multi-image understanding tasks, where relational reasoning across images is crucial. While MiCo also improves over standard baselines on single-image tasks, its performance remains behind models trained with task-specific guidance, particularly in complex scenarios like visual mathematics, where symbolic reasoning and structured representation are essential but not explicitly modeled in our current training paradigm.
Task-wise analysis. We further analyze the performance of MiCo on specific sub-tasks from MuirBench [32] and BLINK [10] to better understand its strengths and limitations. Our contrastive learning framework demonstrates clear advantages on correspondence-styy
tasks, such as Visual Retrieval and Semantic Correspondence, where MiCo outperforms other reasoning models. These results highlight the model's 's strength in aligning multimodal signals through relational comparisons. In addition, Spatial Relation tasks- which evaluate the model's 's understanding of image layout and object positioning- also benefit from contrastive training. By encouraging attention to relative positions among visual entities, MiCo achieves the highest accuracy in this category.
On the other hand, MiCo lags behind models trained with manually curated reasoning datasets on tasks such as Scene Understanding and Forensic Detection, which typically rely on single-image question answering. These tasks often demand domain-specins emphasized in our data construction process. We also observe that Relative Depth, representative spatial reasoning task, remains challenging for models and exhibits noticeable performance drop. This suggests that depth-aware understanding is not sufficiently captured by current training signals. Incorporating explicit spatial or geometric cues remains promising direction for future work toward building spatially grounded rision-language models.
priors or curated logic patterns that are less

---

Question: How many different cups could you find?
Question: How transform the first image to the second?
MiCo:
Let's analyze the differences between Oven Door: In the first image, the oven door is open, revealing the interior where the bread is baking, In the sccond image. the oven door is closed, and can see the bread through the glass window, Lighting: The lighting inside the oven appears brighter in the first image compared
imagess.
MiCo:
To determine how many different can he found, analyze each image:
Image Z: The person holding cup with simple design of coffee beans and steum. This uppears be one type of cup. Image The person is holding cup with
intricate design featuring, swirls and coffee beans. This looks like a different style
image, which might be due open dooe allowing more light to enter.
compared
Background Elements: The second image additional elements the background, such as sign that reuds "SUB" and pink cloth hanging Focus: The focus seems be the bread in both imuges, but the second image provides a clearer view the bread through the closed door.
The here has similar design to the second image, swirls and coffee beans... This suggests that might he the same as the one in Image 2.
Image 3:
are not present
image.
Image shows one type cup. Image shows amother distinct type cup. Image shows
cup Image 2.
Therefore. are distinct types cups across all three images.
Hence, close the door of the oven.
How the comera
shooting video? The differences between the two images suggest that there has been change in the position of the laptop relative
Question: Which point is comeeponning to the reference point?
MiCo:
To determine the correponding point the reference point (REF) the first image, need identify similar point in the second image that could be analogouus. In the first image: The reference point (REF) is located near the top center of the bottlo. to cup area.
MiCo:
rest the scene, particularly conceming its angle and the position items on the table. first imsae, laptop appears be angled towards the viewer, while the be
In the second image:
second image.
Point
the very top the bottle's cap.
upright and centered.
Point is slightly below the cap, the neck the bottle.
these observations, likely the camera moved to capture this change in perspective, Since the the first image from the beginning the video and the second image from end, and considering changes described.
Point around the middle the bottle,
near the label.
bottom
The point that is closest to the top center of bottle. similar where the referenes located the first image. Point Therefore, the answer Point A.
Hence, the camera must have moved to the right to achieve this fruming.
Figure 3: Demonstrations for visual reasoning. Given question, MiCo first examines the details of each image identify answer-related visual cues, and then performs cross-image comparisons to derive the final answer. The reasoning processes are marked in gray, with key contents underlined.
4.5 Qualitative Analysis
We provide qualitative demonstrations in Fig. 3 to illustrate the multi-image reasoning process of MiCo. For each question, the model first generates detailed reasoning chain that carefully compares visual cues across the input images. This includes analyzing fine- one-grained differences and similarities that are relevant to the question. Based on this reasoning, the model then produces a final answer that successfuly addresses the query objective.
4.6 Unsuccessful Attempts
Throughout our exploration, we experimented with several alternative approaches that ultimately did not lead to improved performance. For completeness and to facilitate future research, summarize these unsuccessful attempts and provide insights into why they may have failed.
Confidence reweighting. Since our task is formulated as answering T/F questions, even when evaluating three comparisons simultaneously, there remains a non-trivial chance (12.5%) obtaining the correct answer purely by guessing. To reduce the impact of such randomness, we explored adding an additional reward or weight based on the model's 's answer confidence. Specifically, we experimented with several approaches to compute confidence scores from the softmax probabilities of the output tokens. However, these confidence-bart performance improvements. We analyze that this may be due to the fact that the softmax probability
reweighting strategies did not yield any

---

of the predicted token does not reliably reflect the model's true certainty about the overall answer. In particular, the model may assign high confidence tokens that are syntactically or semantically unrelated to the actual correctness of the reasoning (e.g., punctuation, or irrelevant words within the output). As result, the computed "confidence" can be misleading, making it an ineffective signal for reward shaping.
Importance sampling. As in our Augmented GRPO, we sample the trajectories on simple examples with weak augmentations, but we use the trajectory to optimize harder exaples with strong augmentations. This might cause misalignment similar to offline reinforcement learning. In this way, we apply importance sampling, which calculates the probability gap between the trajectories for the simple and hard examples as a weight to reweight the reward/advantages. This strategy could not bring improvements. We suspect that although importance sampling is theoretically justified, it may interfere with the core optimization dynamics of GRPO. Specifically, GRPO relies on the relative ranking of trajectories within group to compute structured advantages. Introducing importance derived from distribution shifts- may distort this internal ranking or inject instability into the reward signals. Additionally, the token-level probability changes caused by visual augmentations can be noisy or poorly calibrated, making the computed importance weights unreliable in practice.
Conclusion
In this work, we propose self-supervised framework that leverages inherent image constraints to incentivize multi-image reasoning in VLMs. We identify that the core challenge lies in linking visual cues across images. To address this, we adopt contrastive learning principles and construct image triplets for reinforcement training. To further enhance reasoning, we introduce Augmented GRPO, which samples rollouts from simpler examples and optimizes the model harder ones. Although trained solely on image comparison tasks, our model generalizes well and achieves strong results across multiple benchmarks.
Broader Impact. MiCo explores a self-supervised and reinforcement learning-baat approach to improve multi-image reasoning in vision-language models without relying human-annotated question-answar pairs. By leveraging intrinsic visual constraints, such as consistency across augmented views and differences between similar images, MiCo significantly reduces the need for labor-intensive data curation. This has the potential to democratize the development of reasoning- capable Al systems, making them more accessible in low-resource settings for underrepresented languages and domains where curated datasets are scarce.
However, with any powerful vision-language technology, there is a risk of misuse, particularly in applications involving surveillance, misinformation, or unauthorized inference of user intent from visual data. MiCo's improved ability to perform fine-grained comparisons across images could be exploited in privacy-invading scenarios if deployed irresponsibly. To mitigate such risks, we advocate for deploying MiCo in alignment with responsible Al guidelines, ensuring transparency, consent, and clear boundaries in its application domains. In practice, this includes integrating robust sensitive content filtering, restricting deployment in high-stakes or privacy-sensitive scenarios, and establishing human-in-the-lowg mechanisms for critical decision-making processes.
Limitations and future directions. While our approach supports general reasoning through visual comparisons, it shows limited effectiveness on specialized tasks such as face verification, visual math, and spatial understanding, where structured priors domain-specific knowledge are required. In future work, we plan to explore more efficient data construction strategies tailored to these domains.
Acknowledgement. This work is supported by the National Natural Science Foundation of China (No. 624B2124, 62441615, 62422606).
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Reviews:
[{"review_id": "0gnJGL1TK5", "reviewer": "Reviewer_sVvm", "summary": "MiCo proposes a multi-image contrastive learning framework to train visual language models for cross-image reasoning by constructing image triplets (two enhanced versions of the same image + a similar but different image). The model is prompted to generate reasoning procedures to compare these images and is optimized through rule-based reinforcement learning without the need for manually annotated question-answer pairs.", "questions": "refer the weaknesses. I will consider increase my rate within the rebuttal.", "limitations": "Yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. It uses the intrinsic constraints of images as supervisory signals to construct image triplets that are visually similar but have subtle differences, thus avoiding the high cost of manually constructing QA pairs. And Augmented GRPO, which uses weakly enhanced sampling trajectories and optimizes the strategy on strong enhancement.\n\n\n\nWeaknesses:\n1. The method lacks necessary insights, similar to the multi-image version of NoisyRollout\n2. It is necessary to evaluate on more benchmarks to illustrate the superiority of the method and model, such as MuirBench, MMIU\n3. Typo： LVAA-Thinking\n4. In a sense, I think the results in Table 1 cannot explain the superiority of Mico. For example, Eureka is only trained in a single-image scenario, but it is only 3 points behind Mico, which is trained on multiple images.", "was_revised": true, "final_justification": "Thank you for the author's response. After careful reading, I still hold the following views, and I believe this paper is too mundane to provide effective insights. Therefore, I maintain my original score, and I think a score of 3 is very appropriate for this article.\n\nAlthough the authors list the differences from NoisyRollout, they must acknowledge that they are still very similar. For example, crop, resize, and noise are all common techniques in image augmentation, and this minor difference is hardly convincing.\n\nThe improvements on other multi-image benchmarks chosen by the authors are too marginal, with significant improvements only on the benchmark established by the authors themselves. This raises doubts about the effectiveness of the method.\n\n\"We only utilize existing video and image editing datasets without any additional manual processing. In contrast, the main contribution of MM-Eureka is the construction of a training dataset that 'features diverse knowledge domains with human-verified answers and solution processes.'\" This statement is not at all an explanation for why their method only shows marginal improvement over Eureka. If the authors believe that open-source datasets are of poor quality, they should collect high-quality data, such as a multi-image version of MMK12, which would make a greater contribution.", "rebuttal": "``W1: The method lacks necessary insights, similar to the multi-image version of NoisyRollout.``\n\nWe would like to warmly clarify that our Augmented-GRPO is fundamentally different from NoisyRollout. We are aware that the fact “we both use image augmentation” might make us **appear similar**, which is why we have included a detailed explanation in Lines 29–42 of the appendix.\n\n**Here we emphasize the core differences.** NoisyRollout uses image augmentation (primarily Gaussian noise) to increase the diversity of sampled CoTs, and leverages these more diverse CoTs to optimize the original, non-augmented prompts.\n\nIn contrast, we apply more aggressive augmentations (e.g., cropping, resizing) and optimize the strongly augmented samples directly. Our motivation is to use easier samples (with weak augmentation) to obtain better CoTs, and then use these CoTs to optimize the harder samples (with strong augmentation).\n\n\n| Method | Optimized Traj. | Aug. | Motivation. |\n|--------|--------|--------|--------|\n| NoisyRollout  | Non-aug prompts  | Gaussian noise | Increase the CoT diversity(from aug. samples) to optimize the original prompts( non-aug.)   |\n| MiCo(Ours)  | Augmented prompts  | Crop, resize  | Get better CoTs from easier samples(weak aug.) to optimzed harder prompts(strong aug.) |\n\n\n**Concurrent work.**  Augmented-GRPO is only one of our contributions. Our primary contribution lies in exploring self-supervised signals to train rule-based reinforcement learning. Additionally, NoisyRollout was released in April, just one month before our submission deadline. Therefore, it can be considered concurrent work.\n\n---\n\n``W2: It is necessary to evaluate on more benchmarks to illustrate the superiority of the method and model, such as MuirBench, MMIU``\n\n**Results on more benchmarks.** Thank you for your suggestion. We would like to highlight that we have already included additional benchmarks (MuirBench, BLINK) involving multiple images in Table 3 of the main paper. Here, we further include results on MMIU.\n\n| Method | MuirBench | BLINK | MMIU |\n|--------|--------|--------|--------|\n| Qwen2.5VL-7B | 58.43 | 55.54 | 51.67 |\n| Ours |  60.53 |  57.23 | 54.02 |\n\n**Other benchmarks are less suitable.** In fact, MuirBench, BLINK, and MMIU are not ideal for evaluating multi-image reasoning. Although they involve multiple images, they often treat them independently within VLMs. For example, many questions are of the form “How many hands have gloves on?”, where the model does not need to link or compare across images. In contrast, VLM2-Bench is specifically designed to require linking fine-grained visual cues across images, which closely aligns with our objective.\n\n**Detailed sub-track analysis.** We have also provided detailed analysis of the sub-tracks within the general benchmarks in Table 4. The results show that our method brings significant improvements on tasks that truly require comparing different images, such as visual retrieval, semantic correspondence, and more.\n\n---\n\n``W3: Typo： LVAA-Thinking ``\nThank you for your thorough review. We will make the necessary revisions and ensure all details are carefully verified.\n\n---\n\n``W4: In a sense, I think the results in Table 1 cannot explain the superiority of Mico. For example, Eureka is only trained in a single-image scenario, but it is only 3 points behind Mico, which is trained on multiple images.``\n\n**Cost for training data construction.** We would like to emphasize that our main insight lies in leveraging self-supervised signals to train RL models. We only utilize existing video and image editing datasets without any additional manual processing. In contrast, the main contribution of MM-Eureka is the construction of a training dataset that \"features diverse knowledge domains with human-verified answers and solution processes\" (as stated in their paper), which is significantly more costly.\n\nEven without any human annotation or verification, our method still achieves state-of-the-art performance with a margin of 3%. Imagine extending their data construction pipeline to cover fine-grained comparisons across multiple images—the cost would increase substantially (as labeling multi-image data is far more complex), and there is no guarantee that the resulting performance would surpass ours.\n\n**Eureka also include \"multiple images\".** The Eureka dataset also contains examples involving multiple sub-figures, such as comparing different diagrams in a long image or understanding three-view technical drawings. Although these are concatenated into a single image input, such compositions still help the VLM learn to link visual cues across different images.\n\n**We also get higer results on single-image task.** Reinforcement learning naturally possesses generalization capabilities. It is expected that training CoTs on single-image tasks can generalize to some extent to multi-image scenarios. However, by training directly on multiple images, our method also achieves higher performance on single-image benchmarks, such as HallusionBench, MMMU, etc., as shown in Table 3.\n\n**The performance is already high.** Compared with our baseline (Qwen2.5-VL-7B-CoT), we have already achieved a 12.93% improvement, even surpassing GPT-4o. When performance is already high, further improvements become quantitatively more difficult to obtain.\n\n**Concurrent works.**  Additionally, all these works are concurrent works. Eventhough,we add them to discussion and surpasses them with a clear margin(3%)"}, {"review_id": "cSDzZOMFTW", "reviewer": "Reviewer_A4x6", "summary": "The authors propose a method to enable vision-language models (VLMs) to perform multi-image reasoning. They begin by demonstrating that existing VLMs struggle with robust visual comparison. To address this, they construct training triplets consisting of two augmentations of the same image and a third image that is visually similar but semantically different. Using these triplets, they apply an augmented Group-Relative Policy Optimization (GRPO) approach to refine reasoning trajectories. Experimental results show that the method achieves competitive performance across multiple benchmarks.", "questions": "It would be interesting to see more failure cases and corresponding qualitative results. Could the authors also elaborate on the scale of the training data and whether they were manually validated?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- The idea to perform augmented GRPO on image triplets is novel, addresses a well-defined problem (multi-image understanding and reasoning), and does not require manual annotations.\n\n- The authors demonstrate significant improvements over the baseline, showcasing the strength of the method.\n\n- The authors also conduct extensive ablation studies.\n\n### Weaknesses\n\n- The use of synthetic data and augmentations during training could lead to shortcut learning.\n\n- For reproducibility, it would be important to include more details about how the editing pipelines, such as OmniEdit, were used to construct the data (e.g., what types of prompts were used).", "was_revised": true, "final_justification": "Given the rebuttal that addresses my remaining concerns, I retain my positive score.", "rebuttal": "Thank you for acknowledging our paper and for your constructive comments. We will further polish our manuscript according to your suggestions.\n\n---\n\n``W1. The use of synthetic data and augmentations during training could lead to shortcut learning.``\n\n**Augmentation could alleviate shortcut learning.** We would like to clarify that the purpose of adding augmentations is not to create \"pairs with different images,\" but rather to make \"pairs with the same image\" more challenging. Without augmentation, it would be trivial for the model to identify identical images, as their visual tokens are exactly the same. The model could compare two features pixel by pixel, once they find one different pixel, they could conclude they are different images. This may lead to shortcut learning, where the model fails to compare finer details. With augmentations, however, the model can no longer rely on pixel-by-pixel similarity to match images, and is instead forced to attend to semantic content and make the CoT (Chain-of-Thought) reasoning more effective. The results in Table 2(c) demonstrate the importance of augmentation.\n\n**We also incorporate real data from videos.** To mitigate potential biases from synthetic differences, we additionally include video frames, which naturally capture real-world transformations. As shown in Table 2(b), the combination of video data and editing data yields the best performance.\n\n**Experiment results support the effectiveness of synthetic data and aug.** While we acknowledge that synthetic differences may not perfectly align with real-world transformations, reinforcement learning is known for its strong generalization capability. Our experimental results confirm the effectiveness of our approach—achieving state-of-the-art performance on VLM2-Bench and consistent improvements on other benchmarks. Furthermore, Table 2(b) shows that incorporating editing data leads to significant performance gains.\n\n---\n\n``W2. For reproducibility, it would be important to include more details about how the editing pipelines, such as OmniEdit, were used to construct the data (e.g., what types of prompts were used).``\n\n**We use existing datasets.** We would like to clarify that we directly use an existing image editing dataset (OmniEdit), where before- and after-editing image pairs are already provided. This configuration is described in Line 169, and we also perform an ablation study on different editing datasets in Lines 214–218 and Table 2(b). Therefore, our method does not involve constructing editing pipelines, generating prompts, or other manual processing.\n\n**Results on self-constructed data.**  Although we primarily use existing datasets in the main paper, we also follow your suggestion and construct editing pairs ourselves.\nSpecifically, we use OmniEdit to obtain the original images, prompt Qwen2.5-VL-7B to imagine editing instructions (covering basic editing types such as adding, removing, attribute modification, pose change, etc.), and then use Flux-Kontext to generate the edited images. This pipeline yields comparable or even better results.\n\n| Data | General | Object | Person |\n|--------|--------|--------|--------|\n| OmniEdit | 61.23 | 65.33 | 56.35 |\n| UltraEdit |  60.88 | 64.33 | 55.27 |\n| Our Constructed |  61.46 | 65.51 | 56.28 |\n\n**Open-source for reproducibility.**  To enhance reproducibility, we will open-source the training code and data upon acceptance.\n\n---\n\n``Q1. It would be interesting to see more failure cases and corresponding qualitative results. Could the authors also elaborate on the scale of the training data and whether they were manually validated?\n``\nThank you for your constructive suggestions, we would add more qualitative analysis for the failura cases. \n\n**Failure cases.**  We have indeed observed several interesting failure cases caused by CoT overthinking. For example, when asked, “Are there any objects in image1 but not in image2?”, the model sometimes responds: “... There is a boy in image1 who disappears in image2. However, a human is not an object, so there are no objects...” This kind of reasoning reflects the model’s tendency to over-interpret the question. We will include a more detailed analysis of such cases in the revised version. Although such cases are relatively infrequent, addressing them may require providing the model with clearer explanations of the questions and more precise definitions of what is expected in the answer.\n\n\n**Training data.** We currently use 10,000 samples (5K from editing data and 5K from video data). This scale is comparable to other VLM reasoning models, such as Vision-R1 (10K), NoisyRollout (6.4K), ThinkLite-7B-VL (11K), and MM-Eureka (15K).\n\nOur data does not require manual validation, which we consider an advantage. Since we aim to explore self-supervised signals from raw images and videos, we inherently know whether two images are the same or different based on how they are constructed. As a result, our final answers are automatically correct without any human annotation."}, {"review_id": "OgVIshzFjY", "reviewer": "Reviewer_jEYQ", "summary": "The paper presents a post-training framework named MiCo (Multi-image Contrast), to enhance the multi-image reasoning capabilities of Vision-Language Models (VLMs) through reinforcement learning (RL). The major contributions of this work are its task definition for training and the automated pipeline to construct labeled training data.\n\nMiCo trains a VLM to find two augmented views of the same image in a triplet of three similar images (views). The model is optimized to generate chain-of-thought (CoT) reasoning to determine similarities/differences among the triplet. The training pipeline, including prompt format and reward design, is basically borrowed from the DeepSeek-R1 and its GRPO algorithm. Besides, the paper introduces a rollout augmentation strategy that uses weakly augmented views to rollout and strong augmented views to update the model.\n\nMiCo can constructs labeled data for RL training, without the need for human annotations. It constructs image triplets consisting of two augmented views of the same image and a third similar but distinct image, sourced from video frames (e.g., VidGen-1M) and image editing datasets (e.g., OmniEdit).\n\nExperiments show that MiCo, without relying on manual annotated training data, generalizes effectively to diverse multi-image reasoning benchmarks (e.g., VLM2-Bench, MuirBench) and achieves state-of-the-art results.", "questions": "1. The paper does not discuss the data scaling for RL training. What is the minimum amount of data required to train the model, and does the model performance continue to improve with more data and more training steps?\n2. In Section 3.3, the paper mentions \"To increase the diversity and balance the difficulties of questions, besides the image triplet, we also\n142 construct image pairs and design the corresponding prompts for comparing two images.\" What is the detail of this process? How does it affect the model performance?\n3. Do the training data include samples with triple views of the same image? In other words, can the labels be \"TTT\" or \"FFF\", or must have twice \"T\" and once \"F\"? Also, can the training data include samples with only two views? How does these modifications affect the model performance?\n4. In table 1, why is Qwen2.5-VL-7B-CoT marked as non-reasoning model?\n5. Does the Prompt Diversity in Table 2 (e) mean using variants of reasoning prompt (system prompt) or variants of user question template, or both (that are defined in Section 3.3)?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n- The training paradigm of MiCo is simple yet effective. The training task of comparing image triplets is conceptually straightforward and simple. Yet, it is effective in enhancing the multi-image reasoning capabilities of VLMs.\n- MiCo can automatically constructs training QA pairs with image intrinsic constraints, significantly reducing dependency on labor-intensive annotations. Also, it essentially implements a self-supervised learning paradigm.\n- It proposes a rollout augmentation strategy which uses weakly augmented views to rollout and use strong augmented views to update the model. This strategy is shown to be effective in improving the model's reasoning capabilities.\n- The trained model demonstrates strong multi-image reasoning capabilities and also generalizes well to general visual language tasks.\n- The ablation experiments are comprehensive and insightful.\n\n\n**Weaknesses:**  \n- The framework only applies to the Qwen2.5-VL-7B model, which rises concerns about its generalizability to other VLMs, including larger models as well as other model series (e.g., LLaMA, Mistral).\n- The paper lacks discussion on the data scaling for RL training. It is unclear the minimum amount of data required to train the model, and whether the model performance continues to improve with more data and more training steps.\n- The framework relies heavily on high-quality contrastive pairs (from videos or editing datasets). Its performance may degrade with less structured or noisier data sources. This also poses a challenge for the framework if we want to scale the training data.\n- The framework may degrade model's performance on person-centric tasks. The paper does not provide sufficient analysis on this aspect.", "was_revised": true, "final_justification": "The paper provides simple ideas and an effective method to improve MLLM's multi-image reasoning abilities. The experimental results and conclusions of the paper are convincing. Thus, I believe the paper is worthy of acceptance.", "rebuttal": "``W1. The framework only applies to the Qwen2.5-VL-7B model, which rises concerns about its generalizability to other VLMs, including larger models as well as other model series (e.g., LLaMA, Mistral).``\n\n**Previous works also use Qwen2.5-VL.** Thank you for your constructive suggestion. We would like to clarify that nearly all concurrent works on RL-based CoT—such as MM-Eureka, NoisyRollout, and ThinkLite-VL—conduct experiments on Qwen2.5-VL-7B. We follow this common setting to ensure fair comparison and consistency in base configurations.\n\n**Results on more models.** In response to your suggestion, we also evaluate our method on additional base models. Since our framework requires a vision-language model (VLM), pure language models (e.g., LLaMA, Mistral) are not applicable. Therefore, we extend our experiments to different model sizes of Qwen2.5-VL as well as MIMO-VL-7B, and evaluate them on VLM2-Bench.\n\nIn all settings, the base models are prompted to generate CoTs before producing the final answers.Our method consistently brings improvements. The gains are more significant when the baseline is relatively weak, but we still observe clear improvements even with strong baselines.\n\n\n| Model | General  | Object | Person | Overall |\n| -------- | -------- | -------- | -------- | -------- |\n| Qwen2.5VL-3B     | 20.89 | 47.94 | 35.95 | 34.93 |\n| Qwen2.5VL-3B + Ours     | 34.57 |\t57.72\t| 44.66 |45.65 |\n| Qwen2.5VL-32B     | 63.29 | 71.81 | 65.16 | 66.75 |\n| Qwen2.5VL-32B + Ours     |67.42 | 73.21 | 67.22 | 69.28 | \n| MIMO-VL-7B     | 71.26 |68.99 | 59.52 |66.59 |\n| MIMO-VL-7B + Ours     | 74.38 | 70.96 | 58.83 | 68.06  | \n\n* Please note that training 32B models requires significant computational resources. Therefore, we used a smaller group size (4) and trained for fewer steps (100 steps).\n\n---\n\n\n``W2. The paper lacks discussion on the data scaling for RL training....``\n\nThank you for your suggestion. We add ablations for data scaling. In the main paper, we use 10K samples for training to ensure fair comparisons, in line with recent RL works: Vision-R1 (10K), NoisyRollout (6.4K), ThinkLite-7B-VL (11K), and MM-Eureka (15K).\n\nWe report ablation results on VLM2-Bench with varying data scales:\n\n| Num. Data | 0  | 100  | 500 | 5,000 | 10,000 | 50,000  | 100,000 | \n| -------- | -------- | -------- | -------- | -------- |-------- |-------- |-------- |\n| Performance  | 52.20  |  55.02  | 56.25   | 58.89  | 61.61  | 62.53  | 58.32 (sometimes collapse) |\n\nWe provide the following observations:\n* **Small-scale data is also effective.** Even with only 100 training samples, we observe improvements over the baseline. This reflects a typical property of RL—its effectiveness with limited supervision. In addition, our augmentation strategy increases sample diversity, further enhancing learning efficiency.\n* **Scaling law under moderate data regimes.** Increasing the amount of training data within a moderate range leads to steady performance gains, with results improving from 55.02 to 62.53 as the data size increases from 100 to 50K.\n* **Saturation and model collapse at large scale.** When scaling to 100K samples (and training for more steps), performance begins to drop, and sometimes we meet \"model collapse\". We already include KL regularization to stabilize training. We hypothesize that the model may overfit to the task-specific data and lose general capabilities. Given that our task scope is relatively narrow, a more suitable strategy may be interleaved training (as in DeepSeek-R1), which alternates between rule-based RL for specific tasks and supervised fine-tuning (SFT) for general capabilities.\n\n\n\n---\n\n\n``W3. The framework relies heavily on high-quality contrastive pairs (from videos or editing datasets)...``\n\nWe would like to respectfully clarify that a core motivation of our framework is to eliminate the need for costly data preparation. In fact, this is a key advantage of our approach over prior methods, which often rely on expensive and carefully curated datasets.\n\nOur solution does not depend on any specific data source—it can leverage arbitrary video sources or image editing datasets, as long as we can obtain pairs of similar but different images. As shown in Table 2(b), using different editing datasets does not significantly affect the final performance, indicating the robustness of our method to data variation.\n\n---\n\n``W4. The framework may degrade model's performance on person-centric tasks. The paper does not provide sufficient analysis on this aspect.``\n\n| Model | General  | Object | Person |\n| -------- | -------- | -------- | -------- |\n| Qwen2.5VL-7B    |   39.64  | \t53.53  | 63.44  | \n| Qwen2.5VL-7B + raw CoT      |  43.08\t| 50.98\t| 48.52|\n| MM-Eureka    | 51.31  | \t60.20 | 58.00 | \n| NoisyRollout    | 42.38 | \t49.55 | \t54.33 | \n| VLAA-Thinker    | 55.26 | \t62.86 | \t56.81 | \n| ThinkLite    | 43.51 | \t62.52 | \t56.87 | \n| MiCo(Ours)    | 62.13\t|  65.53\t|  57.18 |  \n| MiCo(Ours) + Person Specific    | 61.86\t|  63.90\t|  65.26 |  \n\n**Face verification differs significantly from other tasks.** Person-track focus on the questions related to human ID. However, face verification / Re-ID requires more abstract and fine-grained perception compared to other tasks that benefit from visual cue linking (e.g., object comparison or spatial reasoning). While our method improves tasks involving color, texture, or relative position analysis, these contrastive visual cues may not be helpful for determining identity between two faces—and can even introduce distractions.\n\n**Our results on the person-centric track are competitive.** We report per-track performance across models. Our method achieves 57.18 on the person track, slightly below MM-Eureka but outperforming all other baselines, demonstrating competitive performance.\n\n**CoT generally degrades person-track performance.** We observe that all reasoning-based models show reduced performance on person-centric tasks compared to the base model (63.44). In particular, directly adding a CoT prompt leads to a significant drop (from 63.44 to 48.52). We argue that tasks such as face verification are less suitable for CoT-style reasoning, as they rely more on direct visual representation comparison than on text-based reasoning.\n\n\n**Could be improved by task-specific designs.**  While the performance on person-centric tasks could be improved by incorporating task-specific data (as we add LFW dataset in the last row), our current focus is on developing a general and broadly applicable framework. We intentionally avoid tailoring the data or training strategy to individual task types, in order to preserve the generality and transparency of our method. Instead, we provide an honest analysis of the limitations observed in certain task categories, which we believe offers more meaningful insights for future research.\n\n---\n\n\n``Q1. The paper does not discuss the data scaling for RL training...``\n\nPlease see the answer in W2.\n\n---\n\n\n``Q2. In Section 3.3, the paper mentions \"To increase the diversity and balance the difficulties of questions, besides the image triplet, we also construct image pairs and design the corresponding prompts for comparing two images.\" What is the detail of this process? How does it affect the model performance?``\n\nLiterally, we construct a portion of samples using only two images and ask the model whether they are different. The answer is typically a simple True/False. Since many real-world multi-image understanding tasks essentially involve comparing two images (e.g., finding differences, identifying changes, verifying consistency), including such samples improves the diversity and balance of the training data. As shown in Table 2(d), this design leads to measurable performance improvements.\n\n---\n\n``Q3. Do the training data include samples with triple views of the same image? In other words, can the labels be \"TTT\" or \"FFF\", or must have twice \"T\" and once \"F\"? Also, can the training data include samples with only two views? How does these modifications affect the model performance?``\n\nCurrently, we support TTT/FFF configurations in the video data, as we are able to sample three distinct frames from the same or different scenes. However, image editing datasets typically provide only two different images, so in those cases, we can only construct triplets where all three images are the same (TTT) or where a third different image is inserted.\n\nThis configuration does not significantly affect overall performance. We conduct ablation studies using different variations of the video dataset to analyze this aspect in more detail, as shown below:\n\n|          | General  | Object   | Person  |\n| -------- | -------- | -------- |-------- |\n| Video Data | 60.29 | 64.50 | 55.68 |\n| Video Data(w/o 3 different) | 60.12 | 65.10 | 55.05 |\n| Edit + Video | 62.13 | 65.53 | 57.18 | \n| Edit + Video(w/o 3 different) | 63.31 | 64.92 | 56.50 | \n\n\n---\n\n\n``Q4. In table 1, why is Qwen2.5-VL-7B-CoT marked as non-reasoning model?``\n\nQwen2.5-VL-7B is not trained with reinforcement learning. We simply prompt it to perform reasoning by providing a reasoning-style instruction, such as: \"First output the thinking process in <think></think>, and give the final answer in <answer></answer> tags.\"\nThus, it does not undergo reasoning-specific training, and we categorize it as a non-reasoning model. We will clarify this definition in the revised version of the paper.\n\n---\n\n\n``Q5. Does the Prompt Diversity in Table 2 (e) mean using variants of reasoning prompt (system prompt) or variants of user question template, or both (that are defined in Section 3.3)?``\n\nPrompt diversity in Table 2(e) refers to using different user question templates. Specifically, we use GPT-4o to rewrite multiple question templates with similar meanings to increase variability. We keep the system prompt fixed, following common practice, as consistency is important during inference and evaluation."}, {"review_id": "Qkfe4JJmQO", "reviewer": "Reviewer_Zh8d", "summary": "The paper introduces MiCo, a self-supervised framework to enhance multi-image reasoning in Vision-Language Models (VLMs). Current VLMs struggle with linking fine-grained visual cues across images, especially for tasks requiring spatial alignment, object correspondence, or tracking semantic changes. To address this, MiCo leverages contrastive triplet training and rule-based reinforcement learning. Experiments show MiCo outperforms other SOTA methods on multi-image benchmarks and generalizes to single-image tasks.", "questions": "Why do the results reported in Table 3 differ from those originally reported in the papers for MM-Eureka-7B and NoisyRollout-7B?\n\nI would like to know how many samples the authors collected from image edit and video datasets for training, respectively.", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\nThe paper combines self-supervised learning with reinforcement learning, eliminating the dependency on annotated QA pairs. This approach leverages inherent structural constraints for reward calculation.\n\nMiCo achieves SOTA results on VLM2-Bench, surpassing GPT-4o and other advanced VLMs. It can also generalize to diverse tasks, improving multi-image reasoning and even single-image tasks.\n\nThe paper conducts comprehensive ablation studies to validate the contribution of each component.\n\n**Weaknesses**\n\nThere exists a certain degree of inadequacy in the construction of image triplets. For example, in the lower-left triplet of Figure 2, the first and second images are defined as identical, while the third is defined as different. Yet, compared to the first image, the second image has the person cropped out. While the distinction lies in \"the presence/absence of a license plate,\" I argue that the presence/absence of the person constitutes a more salient difference.\n\nI suspect there may be issues with the authors' SFT setup. Apart from the reported poor performance on the person track, the SFT approach actually underperforms the base model in most cases on both the general and object tracks, exhibiting a significant performance drop. I am curious about how the authors constructed the CoT for SFT. Additionally, I would like to understand why MiCo shows notably lower performance on the person track compared to other reasoning-based models.\n\nThe proposed model does not demonstrate a significant improvement over other SOTA methods (e.g., VLAA-Thinker-7B) on the VLM2-Bench. Moreover, the gains on general datasets are also quite limited.", "was_revised": true, "final_justification": "The authors' reply has clarified certain aspects of my initial concerns; however, some key issues remain unresolved.", "rebuttal": "``W1. There exists a certain degree of inadequacy in the construction of image triplets...``\n\nThank you for the insightful comment. We had similar concerns during development and found cropping to be important both theoretically and empirically. Our reasoning is as follows:\n\n**Cropping mitigates shortcut learning.** Cropping increases the difficulty of the decision, which is **intentional**. Without crop, it is trivial to identify two images as “the same” since their features are exactly identical.Thus, the model may comparing pixel-by-pixel and declaring images “different” upon finding any mismatched pixels. By applying cropping on images labeled as \"same,\" we effectively prevent this and force the model to perform higher-level reasoning to distinguish real content changes rather than relying on superficial visual differences.\n\n**Prompting helps ignore crop-induced differences.** As noted in Line 140, we include instructions like \"Regardless of the augmentation\" to guide the model away from treating cropping as meaningful variation. This, along with the steady reward curves and performance gains under stronger augmentations (Table 3(c)), suggests the model learns to focus on relevant content. In addition, our cropping is not very aggressive, which crops a region with 0.70-0.95 length of the original image.\n\n\n**Cropping is standard in self-supervised learning.** Contrastive methods like SimCLR, MoCo, and DINO use aggressive cropping to create different views of the same image. These practices show that models can learn to focus on semantic consistency despite visual alterations.\n\nIn summary, cropping is a deliberate and validated design choice that increases task difficulty, prevents shortcut learning, and promotes meaningful comparison.  Our experimental results in Table 2(c) also verifies its effectiveness.\n\n---\n\n``W2. I suspect there may be issues with the authors' SFT setup. ...I am curious about how the authors constructed the CoT for SFT. ``\n\n**Details of the SFT setup.**  As noted in Lines 207–208, we apply SFT on our contrastive dataset to directly predict binary labels (“same” / “different”) without using CoTs, and this model fails to generalize to the more diverse tasks in VLM2-Bench. In contrast, using the same supervision, our RL-based method shows much stronger generalization. This SFT setting differs from a typical “CoT cold-start” scenario, which we will clarify in the revision.\n\n**SFT on CoTs.** Following your suggestion, we also conducted SFT with CoT-labeled samples in the third row of the following table. We prompted Qwen2.5-VL-7B to generate reasoning CoTs for contrastive triplets and used rejection sampling to retain only CoTs leading to correct answers. This brings moderate improvements over answer-only SFT, but still lags behind our RL-based solution. The results highlight the challenge of generalizing from binary classification to open-ended reasoning, and further justify the use of RL.\n\n\n\n| Model | General  | Object | Person |\n| -------- | -------- | -------- | -------- |\n| Qwen2.5VL-7B    |   39.64  | \t53.53  | 63.44  | \n| SFT(answer only) | 42.90 | 51.15 | 55.98 |\n| SFT(with CoT) | 46.13\t| 57.26 |\t54.52 |\n| RL(with CoT)    | 62.13\t|  65.53\t|  57.18 |  \n\n---\n\n``W2. Additionally, I would like to understand why MiCo shows notably lower performance on the person track compared to other reasoning-based models.``\n\n\n\n| Model | General  | Object | Person |\n| -------- | -------- | -------- | -------- |\n| Qwen2.5VL-7B    |   39.64  | \t53.53  | 63.44  | \n| Qwen2.5VL-7B + raw CoT      |  43.08\t| 50.98\t| 48.52|\n| MM-Eureka    | 51.31  | \t60.20 | 58.00 | \n| NoisyRollout    | 42.38 | \t49.55 | \t54.33 | \n| VLAA-Thinker    | 55.26 | \t62.86 | \t56.81 | \n| ThinkLite    | 43.51 | \t62.52 | \t56.87 | \n| MiCo(Ours)    | 62.13\t|  65.53\t|  57.18 |  \n| MiCo(Ours) + Person Specific    | 61.86\t|  63.90\t|  65.26 |  \n\n**Our results on the person-centric track are competitive.** We respectfully clarify that our performance is not notably lower than other reasoning models. Our method achieves 57.18 on the person track—slightly below MM-Eureka but outperforming all other solutions—demonstrating strong competitiveness.\n\n\n\n**Face verification differs significantly from other tasks.** Person-track focuses on identity-related questions. Unlike object comparison or spatial reasoning, face verification and Re-ID require fine-grained, abstract perception. While our method improves tasks involving color, texture, or spatial cues, these contrastive signals are less effective—and sometimes distracting—for identifying whether two faces belong to the same person.\n\n\n\n**General CoTs degrade person-track performance.** We observe that all reasoning-based models show reduced performance on person-centric tasks compared to the base model (63.44). In particular, directly adding a CoT prompt leads to a significant drop (from 63.44 to 48.52). We argue that person tasks rely more on direct visual representation comparison than on text-based reasoning.\n\n\n**Could be improved by task-specific designs.**  While the performance on person-centric tasks could be improved by incorporating task-specific data (as we add LFW dataset in the last row), our current focus is on developing a general and broadly applicable framework. We intentionally avoid tailoring the data or training strategy to individual task types, in order to preserve the generality and transparency of our method. Instead, we provide an honest analysis of the limitations observed in certain task categories, which we believe offers more meaningful insights for future research.\n\n\n---\n\n\n\n`` W3. The proposed model does not demonstrate a significant improvement over other SOTA methods (e.g., VLAA-Thinker-7B) on the VLM2-Bench. Moreover, the gains on general datasets are also quite limited.``\n\n**Cost for training data construction.** We would like to emphasize that our main insight lies in leveraging self-supervised signals to train RL models. We only utilize existing video and image editing datasets without any additional manual processing. In contrast, the main contribution of other works are the construction of a training dataset with complicated pipeline(sample collection, labling, etc)  and even with human-verified answers, which are significantly more costly.\n\nEven without any human annotation or verification, our method still achieves state-of-the-art performance with a margin of 3%. Imagine extending their data construction pipeline to cover fine-grained comparisons across multiple images—the cost would increase substantially (as labeling multi-image data is far more complex), and there is no guarantee that the resulting performance would surpass ours.\n\n**They also include \"multiple images\".** Other worlk like MM-Eureka dataset also contains examples involving multiple sub-figures, such as comparing different diagrams in a long image or understanding three-view technical drawings. Although these are concatenated into a single image input, such compositions still help the VLM learn to link visual cues across different images.\n\n**The performance is already high.** Compared with our baseline (Qwen2.5-VL-7B-CoT), we have already achieved a 12.93% improvement, even surpassing GPT-4o. When performance is already high, further improvements become quantitatively more difficult to obtain.\n\n**We get higer results on single-image task without training.** We only train our model on multiple images comparing task, however, our method also brings improvements on many single image understanding benchmarks, and even achieves higher performance then MM-Eureka on HallusionBench, MMMU, etc., as shown in Table 3. We think the improvements on single image benchmarks are already promising considering we only leverage multi-image self-supervised rewards.\n\n**Concurrent works.**  Additionally, please notice that these works are concurrent works. Eventhough,we add them to discussion and surpasses them with a clear margin(3%)\n\n\n\n\n\n---\n\n``Q1. Why do the results reported in Table 3 differ from those originally reported in the papers for MM-Eureka-7B and NoisyRollout-7B?``\n\nThank you for your careful review. As noted in Lines 176–179, we follow the default hyperparameters of Qwen2.5-VL and use the VLMEvalKit codebase for evaluation.\n\nMinor discrepancies may arise because different methods may have modified question prompts or applied custom answer parsing strategies. In practice, we found it difficult to exactly reproduce some reported results due to such variations and potential missing implementation details.\n\nStill, all methods are based on Qwen2.5-VL-7B, and we strictly follow their system prompt formats. We believe our evaluation is fair and consistent under the same settings.\n\n\n\n---\n\n``Q2. I would like to know how many samples the authors collected from image edit and video datasets for training, respectively.``\n\nWe use a total of 10,000 training samples, consisting of 5,000 from image editing datasets and 5,000 from video datasets. This data scale is in line with prior VLM reasoning works—for example, Vision-R1 (10K), NoisyRollout (6.4K), ThinkLite-7B-VL (11K), and MM-Eureka (15K)—ensuring a fair basis for comparison."}]

Author rebuttal:

Meta-review: The paper received Accept, Borderline Accept, and two Borderline Reject ratings. The authors and the reviewers had an extensive discussion. The reviewers initially raised various concerns such as:
- Similarity to prior work such as NoisyRollout.
- Marginal improvement over prior work as shown in Table 1.
- Lack of generalization to other VLMs.
- Reliance on high-quality contrastive pairs. 
- Comparison with Qwen2.5-VL-7B-CoT, which has a noticeable performance drop compared to the base version.

The authors submitted an extensive response, including additional analyses and new results. The works cited by the reviewers are primarily concurrent, so they do not provide grounds for rejection. The authors also provided comparisons with models specifically designed for reasoning and clarified that several existing benchmarks contain a significant proportion of single-image questions. After reviewing the paper, the reviews, and the rebuttal, the AC is convinced that the authors have addressed the reviewers’ concerns satisfactorily. Therefore, acceptance is recommended.
Decision: Accept (poster)