VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Authors: Senqiao Yang
Junyi Li
Xin Lai
Jinming Wu
Wei Li
Zejun MA
Bei Yu
Hengshuang Zhao
Jiaya Jia
Venue: NeurIPS 2025 poster ()
Keywords: Vision Language Models
Reinforcement Learning
Primary area: applications
TL;DR: Efficient Reasoning VLM

Abstract: Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens.
However, we observe that most real-world scenarios do not require such an extensive number of visual tokens. While the performance drops significantly in a small subset of OCR-related tasks, models still perform accurately in most other general VQA tasks with only 1/4 resolution.
Therefore, we propose to dynamically process distinct samples with different resolutions, and present a new paradigm for visual token compression, namely, VisionThink.
It starts with a downsampled image and smartly decides whether it is sufficient for problem solving. Otherwise, the model could output a special token to request the higher-resolution image. Compared to existing Efficient VLM methods that compress tokens using fixed pruning ratios or thresholds, VisionThink autonomously decides whether to compress tokens case by case. As a result, it demonstrates strong fine-grained visual understanding capability on OCR-related tasks, and meanwhile saves substantial visual tokens on simpler tasks.
We adopt reinforcement learning and propose the LLM-as-Judge strategy to successfully apply RL to general VQA tasks. Moreoever, we carefully design a reward function and penalty mechanism to achieve a stable and reasonable image resize call ratio.
Extensive experiments demonstrate the superiority, efficiency, and effectiveness of our method.
All our code and data are open-sourced.

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao Yang"
Junyi Li
Xin Lai
Jinming Wu Jiaya Jia 1.4
Wei Li 3
Bei Yu²
Hengshuang Zhao
CUHK
HKKU
ByteDDnce
*HKUST
Codes and models:
Abstract
Recent advancements vision-language models (VLMs) have improved perfor- mance by increasing the number of visual tokens, which are often significantly longer than text tokens. However, we observe that most real-world scenarios do not require such an extensive number of visual tokens. While the performance drops significantly in small subset of OCR-related tasks, models still perform accurately in most other general VQA tasks with only 1/4 resolution. Therefore, we propose to dynamically process distinct samples with different resolutions, and present new paradigm for visual token reduction, namely, VisionThink. It starts with a downsampled image and smartly decides whether it is sufficient for problem solving. Otherwise, the model could output special token to request the higher-resolution image. Compared to existing Efficient VLM methods that reduce tokens using fixed pruning ratios or thresholds, VisionThink autonomously decides whether to reduce tokens case by case. As a result, it demonstrates strong fine- graigga visual understanding capability on OCR-related tasks, and meanwhile saves substantial visual tokens on simpler tasks. We adopt reinforcement learning and propose the LLM-as- Judde strategy to successfully apply RL general VQA tasks. Moreover, we carefully design reward function and penalty mechanism to achieve stable and reasonable image resize call ratio. Extensive experiments demonstrate the superiority, efficiency, and effectiveness of our method.
Introduction
have achieved remarkable performance in general visual question answering (General VQA) and various real-world scenarios by projecting
Recently, Vision-Language Models (VLMs) 31 30 33 9
and adapting visual tokens into the LLM space 66 1 102 4 However, the performance of VLMs continues to advance, the consumption visual tokens has grown exponentially. For instance, 2048x 1024 image captured by smartphone requires 2,678 visual tokens in Qwen2.5-VL (5), which significantly exceeds the number of text tokens. This leads to substantial memory consumption and notable latency, further constraining the deployment of VLMs edge devices. Therefore, is imperative to minimize the excessive use of visual tokens.
Numerous works visual token reduction have been proposed 71 59 18
22
100 80). Most
approaches prune or merge a fixed number of visual tokens using predetermined thresholds. However, redundancy levels vary across different questions and images, leading natural question: Should we really apply a uniform token reduction ratio across all scenarios?
To answer this question, we simply reduced the image resolution to decrease the number of visual tokens and evaluated Qwen2.5-Ve (55s( performance on several benchmarks. As shown in the left of Fig. we found that for most real-world scenarios (general VQA scenarios), such as MME
"Equal Contribution, c *Correpponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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MME
Model Performance un Muliipl Benchmarks at Different Resolations
10g
OCRBerch
NiMive
2306
3270
$5.1
94.3
PastV 95.0%
N6.7
Sparshil 42.2%
81.5
Vinundgge 95%
ValoiTlisk: 100%
68.7
04.5
69
RedWorld
PO7U
MaabVids
Vacilla 100%
Ressation
Wenk JOCR-Reinted Benohmarks
Strong RRR-Relteed Benchmaris
Visiil Tokens RetsinRars Ratio
Figure 1: Our key observations and VisionThink performance and efficiency. Left: We find that in most general scenarios, even reducing visual tokens by a factor of four results in only minimal performance drop. However, token reduction leads significant performance drop on strong OCR-related benchmarks. Right: Our VisionThink significantly outperforms previous work in both performance and efficiency.
and RealWorldQA, even reducing the image resolution by factor of four, which significantly cuts visual tokens by 75%, has minimal impact on the model's performance. However, as shown in the right of Fig. T for OCR-realted scenarios such ChartQA and OCRBench, which require detailed understanding and OCR-related capabilities, reducing the number of visual tokens leads to a significant drop performance. Based on these observations, we find that most real-world questions do not require high-resolution images with long visual tokens, while a small subset of OCR-related tasks demand such detailed input much. And uniform token reduction ratio should not be applied across all tasks. Therefore, there is significant potential for efficiency optimization if we can dynamically distinguish between samples that require high-resolution processing and those that do not.
In this paper, we propose VisionThink, new EfficientVLM paradigm that leverages the model's reasoning capabilities. Unlike prior methods that process full images and later discard redundant tokens. VisionThink directly inputs reduced visual tokens and allows the model to request the original high-resolution image when needed. This enables more efficient inference in most real-world scenarios, and meanwhile preserving performance on OCR-related tasks.
Although VisionThink offers a promising way to handle samples with varying levels of visual redundancy smartly, it still faces key challenges:
Effective Reinforcement Learning for General VQA. Conventional rule-based reinforcement learning algorithms, typically used to optimize reasoning process, struggle with the diversity and complexity of general VQA. To overcome this issue, propose the LLM-as- approach, enabling semantic matching. Experiments show performance improvement across several general VQA benchmarks, highlighting the potential to extend vision- based reinforcement learning beyond visual math reasoning to broader VQA tasks.
Determine When High Resolution is Worth. To improve efficiency without compromising perfor- mance, the model must accurately determine when high-resolution input is necessary. We achieve this by carefully designing a balanced reward function to prevent the model from collapsing into always requiring high-resolution images always using low-resolution images. With this mecha- nism, Vision Think maintains strong performance on OCR benchmarks while delivering significant speed-ups non-OCR benchmarks, achieving up to 100% for DocVQA.
introduces a new approach to visual token reduction by dynamically determining reduction based on the content of each sample, thereby achieving efficiency gains at the sample level. Consequently, compatible with other advanced spatial-level methods. We hope work sheds new light this area.
Overall, we present simple yet effective Anpliiee-VisoonThiine

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2 Preliminary
2.1 Large Language Models and Reinforcement Learning
Recent progress in improving the reasoning ability of large language models (LLMs) [6, 21] has shown that Reinforcement Learning (RL) an effective training approach. this work, we use Group Relative Policy Optimization (GRPO) [56] our training method. GRPO removes the need for a separate critic model by using group scores to estimate baselines. This reduces computation cost, improves training stability, and leads to faster and more reliable performance gains.
During the training process, GRPO samples a group outputs {01, (2,,02... 02. .G' based on the given question from the old policy Taat and then optimizes the policy model by maximizing the following objective:
(O)
(T(0:)4)
To(o:19)
min
A, clip
1 e A.)
3DxL (No (Toll
G
Aold (o:1g)
Aa (0:19)
(1)
Tre (rilg Tre (Oilg)
D (Tell
log
(2)
(o:g))
(T(0)
where q represents the input questions drawn from the dataset D, and o denotes the generated text response. DxL is the KL-divergence measure, while and / are hyper-parameters. A, indicates the advantage, computed using a group of rewards (r1,72,..., corresponding to the outputs within each group.
2.2 Computation Complexity
To evaluate the computational complexity of VLMs, we analyze key components, including the self-attention mechanism and the feedforward network (FFN). The total floating-point operations (FLOPs) are given by:
Total FLOPPs = 7 x (4nd² + 2n2d d 2ndm)
where denotes the number of transformer layers, n the sequence length, the size of the hidden dimension, and m is the intermediate size of the FFN.
This equation indicates that computational complexity largely determined by the sequence length n. In general VLM tasks, the total sequence length can be expressed as n Maay + fimg + Plquestion where - the number of image tokens-aa is typically much larger than the other two components, often reaching hundreds even thousands. As result, the prefilling stage dominates the total inference time in most VLM scenarios Hence, controlling the number of image tokens is key to achieving VLM efficiency.
Timg
3 Methodology
3.1 Overview
Our objective is to develop a smart and efficient VLM, capable autonomously determining whether the information in the given image is sufficient to answer the question accurately. As shown in pipeline first processes a low-resolution image to minimize the computataion cost. It then smartly requests original high- resolution inputs when the information in the downsampled image is insufficient to answer the question. Ideally, this strategy maintains high performance while sharply reducing computational load. To achieve this goal, must address two key challenges:
Fig. 2
Effective RL on General VQA. Due to the diversity and complexity general VQA, traditional rule-based RI algorithms are not directly applicable. To address this, we propose an LLM-ass-Jugee strategy, which large language model guides and evaluates the RI. training process (Sec. 3.2 We further extend the Multi-Tun Turn GRPO algorithm to suit our setting (Sec. 3.3

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More Frequently
Less Frequently
VIM
Vision Language Model
Round
2 Round
Direct Answer
High resolution needed
Save 75% Visual Tokens
Add 25% More Visual Tokens
Low-resouution Image Token
High-resolution Image Token
Text
Special Token Image Upscaling
Framework of VisionThink. (a) The left image illustrates VisionThink processing an image with resolution reduced by factor of four, where the VLM directly provides an answer. (b) The right image shows case where the model detects insufficient information and requests high-resolution image answer the question.
Figure 2:
Enabling the model to decide when high resolution necessary. The model must learn assess whether downsampled image contains sufficient information answer the question if the original high-resolution image is required. So that the model could balance the efficiency and performance. To this end, we design reward funetion that encourages optimal resolution decisions (Sec. (3.4) and collect training data across multiple resolutions to support effective learning (Sec. 3.5
3.2 LLM-as-Judge for General VQA
Challenges. One of the central challenges in applying reinforcement learning to General VQA lies in evaluating model responses, especially when answers are open-ended or context-dependent Most existing multi-modal RL efforts remain limited to structured tasks such as visual math, where ground-truti answers be easily defined and verified via rules exact matching. However, this approach breaks down in General VQA settings, where the diversity and ambiguity of valid answers make rule-based verification infeasible.
Pure Text Accuracy Judgement. To address this, we employ an external LLM as a judgment evaluator. Leveraging its broad knowledge and language understanding, the LLM assesses the correctness model outputs a human-aligned and flexible manner. Importantly, the evaluation is conducted purely in text by comparing the model's answer with the ground-truth. This design avoids biases from visual content and the limitations VLM performance. Furthermore, to minimize potential misjudgment by the evaluator, the reward is discrete (either 0 or 1) rather than continuous. The detailed judgment prompt is shown Appendix B.
Effectiveness. The LLdd-e-Jugge is flexible, one advantage is that most of the SFT data could be used. To verify the effectiveness of our proposed LLM-as-Judge, we collected 130K samples (filtered from the open-sourced datasets), which can be directly used to train the model with GRPO, without requiring any cold-start process. The results show significant improvement compared the base model. 1ree2.55VLInnstruc Further details are provided in Appendix B.5
3.3 Mutli-Turn Training Algorithm
Multi-Turn GRPO. In our VisionThink framework, we first input the question and the downsampled image into the VLM. If the information insufficient to answer the current question, the model will autonomously request higher-resolution image and generate a new response. This process is
essentially multi-turn interaction. Therefore, we extend the original GRPO (Eq. T to multi-turn GRPO, as shown in Eq.

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(3) Impact of Penalty Ratio
(b) Visualization Example
Figure 3: (a) Impact of the Penalty Ratio. Applying penalty all resize image requests removing the penalty entirely will both lead to model collapse. (b) VisionThink correctly solves OCR related problems by autonomously requesting high-resolution images.
I
G
l
[GRPO(0) = =E (a.
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G
Elal I(or,)
x=1
(3)
-min Pr. Pr 1 + Arts {
- F
where Pt, = 10 and T(o)) is the token loss masking operation such that T(or)) =
Of is the generated token from LLM and [(o:)= 0 if o is the response token from the called tools. Intuitively, we masked all text and image tokens from the user and performed optimization solely based on the multi-turn output tokens generated by the VLM.
How does the model signal the need for a high-resolution image? To determine when the model requires high-resolution image, modify the prompt to instruct the model to output specific special tokens. Notably, this is a non-trin anivtrivial process because our training does not introduce any cold-start phase, which leads to performance drop in general VQA (Appendix C.2 Therefore, selecting an appropriate and effective prompt at the early stage training is crucial. The prompt must ensure that the model is capable of outputting the required special tokens during multi-turn rollouts a zero-shot setting. Otherwise, GRPO will fail to optimize correctly due to the absence of gradients. We conduct comparative ablation studies in Appendix C.3 and find that the Agent Prompt recommended by Qwen-2.5VL [5] suits Vision Think best. The prompt details are provided in Appendix B.I
3.4 Reward Design
Different reward functions can lead the model toward different optimization directions and final performance outcomes. The reward function in VisionThink framework consists of three compo-
nents:
Rovenal Raccuracy Riomaa - Poantol:
where represents the reward and represents the penalty.
Accuracy Reward, We utilize the LLM-as-Judge strategy to evaluate whether the generated answers correct, where 0 denotes an incorrect answer and denotes correct one. The detailed design of the accuracy reward follows the description in Sec. 3.2
Format Reward. To maintain the model's instruction-follovii capability and ensure that the trained model can more accurately call the image resize function, we apply a format reward. Specifically. require the reasoning process to be enclosed in "<think></thnnk>" tags, the final answer in "<answer></answer>" tags, and the function call to conform to the JSON format specified in Appendix B. If any of these formats are incorrect, the format score is O. Only when all formats correct can the model achieve the full format score of 0.5.
Penalty Control. The design of the penalty is key component of the reward function. As shown in Fig. a), since using high-resolution images generally improves performance, without any penalty, the model tends to collapse into always requesting high-resolution images. To prevent this, we initially
5

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Table 1: Effective Performance Compared to the Sota Model. Our model is based Qwen2.5- VL-7B-Instruct. Thinn]T represents a model trained on general VQA tasks using full image resolution with the LLM-as-Judee strategy, which does not contain efficiency capabilities. Qwen2.5- VL-7B* reports the results evaluate by Imms-eval
MMMU MMMU-Pro MMBench RealWorldQA POPE MME MathVista Verse MMVet
Method
val
test
en lest
test
test
test
testmini
testmini
lest
Closed-Source Model
GPT-4o 50
69.1
54.0
83.4
58.6
85.6
23299
63.8
50.2
69.1
Claude-3.5 Sonnet
68.3
59.9
41.2
Semini-1.5-PPg
62.2
73.9
70.4
88.2
Open-Source General
Cambrain-1-88 65
42.7
75.9
60.0
86.4
1803
IntemVLL2-8B 12
49.3
32.5
81.7
64.4
84.2
2210
58.3
60.0
ViiV-nneVisioon1
08
48.8
66.3
88.4
1998
63.2
57.5
MiniCPM MEEPPM--llamn Llama 2.5, 88
89
45.8
19.6
77.2
63.0
86.7
2025
54.3
MiniCPM-V-2.-88B
99
49.8
27.2
78.0
65.0
83.2
2348
60.6
IXC-2.5 95
42.9
82.2
67.8
2229
63.8
51.7
IntemVL2.5-8B
56.0
38.2
84.6
70.1
90.6
2344
64.4
39.5
62.8
Reasoning Model
LLaVA-CoT-IIB
75.0
LLaVA-RcaSORI-S
50.6
Insight V-8B 14
50.2
24.9
82.3
2312
59.9
Mulberry-B 86
55.0
2396
63.1
19
2190
62.7
27.1
VisionThink
Qwen2.5-VL-7B
5
50.3
37.7
82.6
68.6
86.7
2316
68.2
46,3
61.6
VisionThink
$1.0
40.1
82.9
68.6
87.9
2307
71.2
48.8
67.5
VisionThink
51.2
38.9
80.0
68.5
86.0
2400
67.5
48.0
67.1
followed Search-R1 [23] and applied 0.1 penalty for correct answers that relied on nigh-resolution images. However, this approach causes the model to favor direct answers, leading a collapse where the model relies solely on direct answers, as indicated by the purple line in Fig. The reason is that even blurry, low-resolution images sometimes allow the model to guess the correct answer, and the 0.1 penalty unintentionally reinforced this preference for direct answering.
To address this, we introduce threshold control the phenomenon of "lucky guesses". When the probability of correctly answering with a low-resolution image is low, we apply a 0. penalty to direct answers encourage nigh-resolution requests; conversely, when the probability is high, we penalize high-resolution requests with 0.1 penalty. In summary, the penalty is designed as below:
Cdirect
P Poontrol 0.1 [[drrea 0) lnigh!(( 2 0))
T Cairect + Chigh
(5)
where Cairect and Chigh are the correct-answer counts for low- and high-resolution inputs, respectively, and action is the indicator the chosen action, and we set as 0.2 here. We will discuss the impact
of the threshold in Appendix C.3
3.5 Data Preparation
To enable our model can decide when high resolution is necessary, we collect corresponding VQA samples, including both cases requiring high- nigh-resolution images and cases adequately answered using downsampled images. To achieve this, we use our base policy model, @wwen2.5VL.IIntmnee to perform multiple rollouts the training dataset and classify the samples based on accuracy. Specifically, we set the temperature and roll out each sample times. If both the high-resolution and downsampled images yield correct answers in all 8 rollouts, we classify the sample solvable

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using low resolution. Conversely, if the number of correct answers using the high-resolution image exceeds that of the downsampled image by 6 or more. we classify the sample as requiring high resolution. By using the above method, we selected 10K samples that require high-resolution images and 10K samples that do not, to train our model.
4 Experiments
4.1 Evaluation Setup
We evaluate VisionThink on several general VQA benchmarks, including ChartQA [4]. OCRBench (37), Math Vista [42], MMVet (9], RealWorldQA (74), and Math- Verse 1961 etc. Notably, benchmarks such as ChartQA, OCRBench, and Math Vista are strongly OCR-related, requiring the model to possess high level of detail comprehension. The detailed descriptions of these benchmarks are shown in Appendix B.4
Benchmarks.
Implementation Details. We conduct experiments based on Qwen2.5- S--VLL-B--Insrucce training, we employ veRL_[58] framework and use total batch size of 512, with a mini-batch size of 32, we set the policy LLM learning rate to le 6 and sample 16 responses per prompt, ensuring a stable and effective training process. For inference, we use the vLLM framework and set the temperature to O. Further details are shown in Appendix B.3
For
4.2 Reinforcement Learning Enables VLM to Be More Effective
Main Results. To demonstrate the effectiveness of our VisionThink, we compare our VisionThink with the current open-source and closed-source source offte-of-the-ee (sota) method. As shown in Table O VisionThink is used to demonstrate the effectiveness of the LLM-as-Judge strategy on general VQA tasks. represents a model trained with full image resolution using only accuracy and format rewards, and thus does not incorporate efficiency capabilities. The results show that our VisionTThin achieves comparable or even superior performance on general VQA tasks while being more efficient. Specifically, Math Verse and MMVet achieve scores of 48.0 and 67.1, representing improvements of 3.7% and 8.9%, respectively, over the base model. Furthermore, our model performs comparably to closed-source models several benchmarks such as Math Vista and MMBench, and even surpasses all -oosee- models on MME, achieving a score of 2400. Besides, as shown in Fig. (()). by introducing the LLM-as- Judge for test-time scaling, VisionThink's answer outperforms the vanilla model's 's short direct answer. Moreover, we scale up the data size to 130K, and further demonstrate the effectiveness of LLM-as-Judge General VQA Tasks. The results are shown in Appendix B.5
4.3 Reinforcement Learning Enables VLM to Be More Efficient
Comparison with the Reasoning Model. To demonstrate the efficiency of our model, first compare our VisionThink with QwenRL and QwenRL 1/4, both of which are reasoning models trained using the LLM-as-Judee Judge strategy based on Qwen2.5- VLLBB Instruct. QwenRL and QwenRL 1/4 represent inference using the full-resolution image and the 1/4-resolution image, respectively. As shown in Fig. 4 compare the inference time costs of the three models. Notably, the reported inference times reflect the actual time consumed during vL..M inference, which we believe best rep- resents efficiency in real-world applications. The results show that on most benchmarks, inference time is close to that of QwenRL 1/4, which uses 1/4 of the image tokens, and significantly better than the QwenRL model that processes all image tokens. Specifically, on the DocVQA bench- mark, our VisionThhink model is more than twice as fast as QwenRL. It also outperforms the baseline by approximately one-third in terms of inference time on benchmarks such as MME and POPE. is worth noting that on strongly OCR deeeendedddnt benchmarks like ChartQA, our model consumes more time than the baseline QwenRL. This because VisionThink identifies that most questions cannot be answered correctly at low resolution and thus autonomously requests high-resolution images. As result, the total number of image tokens used by VisionThink exceeds that of the baseline, which we consider reasonable. However, such strongly OCR OCR-dependent benchmarks are relatively rare, so the overall efficiency of VisionThink remains high.
model's
Comparison with the Previous Efficient VLM. To further show the effectiveness of our Vision- Think, compare it with the previous Efficient VLM method FastV and Sparse Notably, all
7

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Qwen-RL
DecVQA
Qwen-RL (1(4)
1355
VisionThine
1109
Benchmark Performance
@wwen-RL. Qwenn-RL (1/4)
#111 943 94-4
2315
MME
420
K2.3
96
788
74,8
653
447
ChariQ
746
253
OCRBench
132
DocVQA
POPE
MIME
CharQQA
OCRBench
Inference Time Cost
Figure 4: Inference Time Cost (seconds) and Benchmark Performance Comparison for Rea- soning Model. Qwen-RL and Qwen-RL (1/4) represent leveraging the LLM-as-Judge the Dwen2.5-VL-Insstre Model and inference on full resolution image and 1/4 resolution image, respec- tively.
these methods require computing attention to optimize with FlashAttention2 and may lead to increased memory usage. Furthermore, they are directly compatible with the efficient inference framework vLLM. Therefore, to ensure fair comparison, we evaluate model performance while keeping visual token consumption as consistent as possible. As shown in Table 2 our VisionThink outperforms previous methods on average across nine benchmarks. Furthermore, previous approaches require predefined pruning ratio threshold, whereas our method can autonomously decide whether to reduce tokens based on the question and image content. As result, on DCR-Related benchmarks such as ChartQA and OCR Bench, our method significantly surpasses FastV and SparseVLM by 9.0% and 8.3%, respectively.
prune visual tokens, which makes them difficult
4.4 Reinforcement Learning Enables VLM to Be Smarter
93%
62%
ChariQA
OCRBench
MME
DocVQ4
38%
Aaple
@ Aaply Rediee Dirvet AROREZ
Anpls Redze Dirant Anouer
Strong OCR-Related Benchmarks
Weak OCR-Related Benchmarks
Figure 5: Vhiio smartly determine the high- resolutioo image ratio. Apply Resize indicates that the model autonomously requests to view the original high-resolution image, while Direct Answer indicates that the model is able answer the question using only the 1/4-sized image.
In this section, we present the proportion of samples across different benchmarks for which our VisionThink gives direct answers versus those for which it requests high-resolution images. This illustrates the model's ability to smartly determine whether the information in the downsampled image is sufficient. As shown in Fig. S we observe that on benchmarks such as ChartQA and OCRBench, which require detailed visual understanding, our model shows higher ratio of requests for righ-rcsolution images. In contrast, for benchmarks like MME and Doc at least 70% of

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Table 2: Comparison with Traditional Efficient VLM Methods. Vanilla represents the Qwen2.5- VL-7B-Instreau The retained ratio of the baseline methods is a predefined hyperparameter, while for VisionThink, the ratio is determined autonomously by the model and reported as statistical value. Note that Down-Sample refers to the model's performance when directly fed images with their resolution reduced by half. Additional baseline comparison results (VisionZip (80) are shown in Table. 7
CharlQA OCRBeach DocVQA
MME
MMVet RealWoridQQ
POPE Mathaissa Math Verse
Method
Avg.
test
lest
val
test
test
testmini
testmini
Retain 100% Visual Tokens Acrost All Benchmarks
79.8
81.5
95.1
2316
61.6
68.6
86.7
68.2
46.3
Vanilla
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Retain 25% Visual Tokens Across All Renchmarks
62.9
68.8
94.3
2270
54.5
68.8
82.8
62.2
43.1
Down-Sample
92.1%
78.8%
84.4%
99.1%
98.0%
88.5%
100.3%
95.5%
91.2%%
93.1%
Retain 50%
Visual Tokou Across All Renchmarks
73.2
75.6
66.8
2282
51.5
68.4
85.5
66.6
45.1
SparseVL.M VI.M 1DX
92.2%
91.7%
92.7%
70.2%
98.5%
83.6%
99.7%
98.6%
97.6%
97.4%
72.6
75.8
93.6
2308
52.8
68.8
84.7
63.7
45.0
V
95.8%
91.0%
93.0%
98.4%
99.6%
85.7%
100.3%
97.7%
93.4%
97.2%
Renain 70%
Visual Tokens Acrous All Beneimarks
75.8
79.3
68.7
2276
53.7
68.5
85.4
66.3
45.1
VLM (ICML 2025)
93.6% %
94.9%
97.3%
72.2%
98. 3%
87.2%
99.8%
98.5%
97.2%
97.4%
77.2
82.2
94.4
2342
56.0
68.6
85.9
65.9
46.9
FastV (ECCV 2024)
98.4%
96.7%
100.8%
99.3%
101.1%
90.9%
100%
99.1%
96. 6%6
101.3%
Retain Approximmaelyy
51.3%
Visual Tokens Across All Benchmarks
79.8
80.8
94.4
2400
68.5
67.1
86.0
67.5
48.0
VisionThink
101.4%
100%
97.8%
99.2%
the samples can answered directly using low-resolution images at 1/4 of the original resolution. These results align with human intuition: most daily questions do not require high-resolution images, and only
tasks truly depend on them. Furthermore, to better demonstrate the 'smart' capabilities of VisionThink, we conduct case studies in Appendix D
4.5 Relationship of the Efficient VLM methods and Vision Think.
Key Differences. Traditional EfficientVLM methods take redundant image as input and attempt remove the redundancy during inference. However, this process typically relies on fixed thresholds, which may yield acceptable performance on standard VQA tasks but result in poor performance on OCR-related or detail-sensitive scenarios, limiting their practical applicability. In contrast, VisionThink inputs reduced visual tokens and enables the model to autonomously determine whether a nigher-resolution image is needed. Ideally, this approach avoids any performance degradation.
Integration Potential. Our proposed VisionThink essentially introduces new paradigm for reading images, which can be integrated with existing Efficient VLMs. In this paper, to provide a straightforward validation of VisionThink, we chose to use image resizing perform token reduction. We believe that adopting more advanced token reduction techniques could further improve the model's direct answering accuracy, consequently, enhance its overall efficiency. Further discussions shown in Appendix C
Related Works
Vision Language Model Reasoning. With the advancement of LLM reasoning capabilities
17
many studies have aimed to improve the reasoning abilities of VLMs [98] 48 43]. One common approach is using -hain-of-Thougle (CoT) prompting to construct SFT datasets. However, the CoTs generated often lack natural human cognitive processes, limiting their effectiveness and generalization. 17 some studies have attempted to transfer this reasoning 85 However, most current approaches remain limited to the
Furthermore, inspired by DeepSeek-RP R1
paradigm to vision tasks [87 visual math and fail to generalize to general VQA tasks. In contrast, VisionThink successfully applies

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effective reinforeement learning to general VQA by leveraging the LLM-as-Judge strategy. Due to space limitations, additional related work on efficient VLMs and LLM- reasoning is presented in Appendix A
6
Concluding Remarks
6.1 Summary
In this work, we introduce VisionThink, novel paradigm for General VQA that enhances efficiency and performance. By initially processing a downsampled image and using reinforcement learning to selectively upscale to higher resolution when needed, VisionThink optimizes computational resources while preserving accuracy. Leveraging the LLM-as-Judge strategy and a tailored reward function, our approach outperforms prior state-of-the-art models across diverse VQA benchmarks, particularly in tasks requiring fine-grat teee-gained details like OCR. We believe Vision demonstrates the potential of reinforcement learning in vision-langue models and encourages the development of more effective and efficient Al systems.
6.2 Limitations and Future work
In this work, we focus the setting of 2x resolution upscaling and most two turns of conversations and yield promising results. However, it has not been extended to the setting flexible resolution upscaling. Besides, incorporating more visual tools such as cropping would further bring benefits in both efficiency and performance. Furthermore, multi-turn turn (for example, more than 5 turns) image tool calls could gain more in solving complex visual problems.
Additionally, our paper utilizes image resizing to reduce the number of visual tokens. This simple method achieves a good balance between performance and efficiency via reinforcement learning. We hope this work inspires further research the field of efficient reasoning vision language models, especially making models smarter and more human-like We will continue to explore the path toward building more general, powerful, and efficient saiguala models.
Acknowledgements
This work supported in part by the Research Grants Council under the Areas of Excellence scheme grant AoE/E--60122-R and the National Natural Science Foundation China (No. 62422606, 62201484).
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Contents
Related Works
18
A.1 Efficient Vision Language Models
18
A.2 Large Language Model Reasoning
19
A.3 Vision Language Model Reasoning
19
B
Additional Experiments
19
B.1
Prompt Details
19
Details of the Format Reward
20
B.3
Implementation Details
21
4
Benchmark Datasets and E Evaluation Metries
22
B.5
Scaling-up Reinforcement Learning on General VQA Tasks
B.6
Comparison with Previous Efficient VLM
25
B.7
Additional Discussion Experiments
26
Further Discussions
28
C.1
Why Use RL Instead of SFT?
28
Why not Cold-Start?
29
Different Prompt Impact
30
Ablation Study Penalty Control Threshold
31
The Bias Influence of LLM-as-Judee
32
C.6
Discussion about Zoom-in Strategy
32
Qualitative Results
33
Broader Impact Statement
33
A Related Works
A.1 Efficient Vision Language Models
Large Language Models (LLMs)) have demonstrated remarkable progress in language understanding Building on their success. VLMs have rapidly advanced by
and generation [1] 66, 4 51 13 29
Prominent models such as LLaVA 33 utilize visual encoders followed by the projection layers to convert images into token sequences compatible with LLMs.. However, as the performance continues to improve, the number of visual tokens grows rapidly, leading to increased computational costs. This trend limits the practical deployment of such models in scenarios like edge computing, autonomous driving, medical analysis, and robotics [24 36 54 81 83 89 55 Therefore, is imperative to avoid the excessive use of visual tokens.
integrating visual information into LLM architectures BB
34 31
35 102 26 82
languae models
61
52
99
53 68 84)
18 80 101 have also recognized the redundancy in visual tokens and proposed various methods to address Most of these works input images containing redundancy and use the attention scores assigned by the model to prune or merge tokens for token reduction. Furthermore, they typically apply a fixed threshold to reduce the same proportion of redundant tokens across all data samples. Although these methods maintain good performance on general VQA tasks, they perform poorly on OCR-related benchmarks. contrast to previous works, proposed Vision Think initially inputs reduced tokens and allows the model to autonomously
Recently, some studies 8 100 77 71 59
18

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determine whether token reduction is sufficient or if a high-resolution image is required. Through this approach, our method achieves efficiency while maintaining strong performance on OCR-related benchmarks. Additionally, VisionThink is not specific token-level reduction strategy but represents a new paradigm that can be integrated with existing EfficientVI.M methods.
A.2 Large Language Model Reasoning
have signifi- cantly improved their reasoning capabilities through methods that simulate human-like stepwise thinking. One foundational technique, Chain-of-Thought (CoT) prompting [69]. encourages mod- els decompose complex tasks into intermediate steps, enhancing performance on a variety of reasoning benchmarks. Furthermore, researchers have explored more structured and dynamic reason- ing paradigms, such as Tree-of-Thought and Graph-of-Thought [8 77, which organize reasoning as branching or interconnected processes. Complementary approaches like Program-of thouught (PoT) [TO] further improve reasoning fidelity by integrating external computational tools verify or simplify logic steps.
Recent advances in large language models (LLMs) 70
53
73
62 79
Besides, recent work has also shifted attention from model architecture design and train-time scaling to test-time scaling [60]. such as Monte Carlo Tree Search (MCTS) [76]. stepwise preference optimization (27]. and reinforcement learning 44 are used to refine outputs during inference. Models such as DeepSeek-RI [17]. OpenAI-O1
49 demonstrate the effectiveness of combining large-scale RL with reward functions that prioritize both correctness and reasoning quality. Although LLMs have shown remarkable progress in structured reasoning, extending these abilities to Vision Language Models remains an open challenge.
A.3 Vision Language Model Reasoning
With the advancement of LLM reasoning capabilities 17].
many studies have aimed to improve the One common approach is using Chain-of-TTe Th-ouughhtt (CoT)
reasoning abilities of VLMs 98 48 43
prompting to construct SFT datasets. However, the CoTs generated often lack natural human cognitive processes, limiting their effectivvness and generalization. Furthermore, inspired by DeepSeek-RI [17].
several studies have attempted to transfer this reasoning paradigm to vision tasks [87 64 20 85
38 57
40 39] Most of these efforts, by collecting CoT data perform cold start and then training the model using a reinforcement learning strategy such as GRPO. While this approach achieves performance improvements specific tasks, it significantly degrades the model's general performance. Moreover, current methods remain limited to visual math or segmentation tasks, failing generalize broader general VQA tasks. In this paper, we propose VisionThink which effectively applies reinforcement learning to general VQA tasks by leveraging the LLM-aa-Judge strategy.
B Additional Experiments
B.1
Prompt Details
B.1.1
LLM-as-Judge Prompt Design
In this section, we detail the prompt design for our LLM-as-Judge strategy. As shown in Table B the placcholders Ground Truth and Prediction dynamically replaced with the corresponding question, ground truth answer, and model prediction during evaluation. Specifically, the judgment process is conducted entirely in text. Our findings indicate that, compared to VLMs, current LLMs achieve higher judgment accuracy and exhibit fewer hallucinations. Moreover, by eliminating the need for visual token inputs, it significantly reduce the overall evaluation cost.
Furthermore, we require the LLM to return a discrete value, with indicating a correct prediction and 0 indicating an incorrect one, rather than a continuous score representing the degree of correctness. This binary format further reduces the likelihood of misjudgment. In a user study of 1,000 cases, no misclassifications were observed.
19

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Table 3: Judgment Prompt Template. Question, Ground Truth and Prediction are dynamically replaced with the specific question, ground truth and model prediction during evaluation.
SYSTEM PROMPT:
You are intelligent chatbot designed for evaluating the correctness of generative outputs for question-answer pairs.
Your task is to compare the predicted answer with the correct answer and determine if they match meaningfully. Here's how you can accomplish the task:
INSTRUCTIONS:
Focus on the meaningful match between the predicted answer and the correct answer.
Consider synonyms or paraphrases as valid matches.
Evaluate the correctness of the prediction compared to the answer.
USER PROMPT:
I will give you a question related to an image and the following text as inputs:
1. ** Question Related to the Image**; Question
** Ground Truth * Ground Truth
3. ** *Model Predicted Answer** Prediction
Your task is to evaluate the model's predicted answer against the ground truth answer, based on the context provided by the question related to the image. Consider the following criteria for evaluation:
**#eeevance** *; Does the predicted answer directly address the question posed, considering the information provided by the given question?
** **Acuracy*** Compare the predicted answer to the ground truth answer. You need evaluate from the following two perspectives:
(1) If the ground truth answer is open-ended, consider whether the prediction accurately reflects the information given in the ground truth without introducing factual inaccuracies. If it does. the prediction should be considered correct.
(2) If the ground truth answer is definitive answer, strictly compare the model's prediction to the actual answer. Pay attention to unit conversions such as length and angle, etc. As long as the results are consistent, the model's prediction should be deemed correct. **Output *
Your response should include an integer score indicating the correctness of the prediction: I for correct and for incorrect. Note that means the model's prediction strictly aligns with the ground truth, while 0 means it does not.
The format should be Score: 0 or
B.1.2 VisionThink Image Resize Prompt
As shown in Table 4 we present the detailed system and user prompts used in our proposed Vision- Think. Specifically, we integrate image resizing as a tool-call function. Following the Qwen2.5-Vi cookbook [6) we employ image resizing. This prompt design allows the model to exhibit distinct behaviors such as requesting image resizing or directly answering the question. These behaviors introduce differentiable gradients, which make it feasible to apply the GRPO algorithm. Furthermore, we analyze the impact of different prompts in Sec.
Agent Prompt that enables the model to output special tokens to trigger
B.2 Details of the Format Reward
The format reward has a total score of 0.5, which awarded only when all formatting requirements are fully satisfied. Specifically, as shown in the VisionThink Prompt (Table. 4 the first requirement that the model's output must include both the the <answer></answer> and <think><</hinkk tags, which denote the final answer and the reasoning process, respectively. The second requirement states that for responses involving an image resize operation, the model must output a correctly formatted <tool call></tool call> tag containing valid JSON content.
20

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Table VisionThink Image Resize Prompt Template. Question will be replaced with the specific question during training and inference.
SYSTEM PROMPT:
You are helpful assistant.
# Tools
You may call the function tool shown below assist with the user query. You are provided with the function signature within <tools></hools> XML tags:
<tools>
"type": "function".
"function":(
"name for human": "resize immge",
"name": "resize image",
"description": "Resize the image resolution.",
"parameters":
"properties": {
"action":
"description": "The action to perform. The available actions are:
resize: Double the resolution of the current image. You should only use this tool if you are unable obtain the critical information needed to answer the question from the current resolution.".
"enum": ["resize"], "type": "string"
"required":: ["action"),
"type": "object",
}.
"rrg format": "Format the arguments as a JSON object."
</tools>
For each function call, return a json object with the function name and the corresponding argument within <tool cooooll/l>//oool__ _all_ccllll__ XML tags:
<tool_call ( ("name": <function-name>, "arguments": <args-json-ohjett>) </tool_call>
USER PROMPT:
Answer the question based on the image provided. You must conduct reasoning within <think> and </think> first in each of your reasoning steps. You may call ONE function tool per step to help you better solve the problem. Place the function tool within <tool_call> and </tool_caat caool_call>>>> at the end of each step to perform a function call. You should continue your reasoning process based the content returned by the function tool. Once you confirm your final answer, place the final answer inside <answer> and </ /aanwwr>>, For mathematical or multiple-choice problem, wrap the answer value choice with \boxed(). Here the image and question: Question.
B.3 Implementation Details
Training Details. In this paper, we conduct experiments using base model, trained with the veRL framework (58). We use a total batch size of 512 with mixed- precision (FP16) training. The mini-batch size is set to 32, and the KL divergence coefficient is 0.001. The policy model is optimized using an initial learning rate of 1 x 10-6, 66 For each prompt, we generate 16 candidate responses using temperature of 1.0, and apply duplicate and empty response filtering, similar to DAPO 90]
(5] as the
Inference Details. In this paper, use the Imms-eval 94
evaluate the model's performance. Besides, in order to save the GPU memory and improve the inference speed, we utilize the vL.LM 25 framework and set the temperature to zero for inference.
21

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Republicans and Democrats say they can't agree on 'basic facts'
W haa'ss the gap percent between the bad and the better scenarios being studied?
Question:
On important issues facing the country, most Republican and Democratic ...
Not only disagree over plans and policies, but also agree basic facts
GT: 47
Pred1: 47
73%
Can agree on basic facts, even they often disagree over plans policies
Pred2: 46
Pred3: 47%
Souroe: Survey adults conducted Sept. 3-15, 2019
PEW RESEARCH CENTER
Pred3: 0.47
Figure An example illustrating the original evaluation method used in ChartQA.
B.4 Benchmark Datasets and Evaluation Metries
We conduct experiments on these widely used visual understanding benchmarks.
benchmark designed to evaluate how well multimodal models answer questions about charts, emphasizing both visual understanding and logical reasoning. includes various chart types, such bar charts and line graphs, with mix of human-written and automatically generated questions to assess complex reasoning abilities. Notably, ChartQA is a strongly OCR- dependent benchmark that requires tne-grained visual understanding, as models must extract textual information from charts and reason over it.
ChartQA. ChartQA
45
def _t _to_floae float (text: str); try:
if text endswith (at"with(""")): ("*"):
return float (text. (text.retrip( / 100.0
else:
return float (text)
except ValueError:
return None
prediction_floo _t float (prediction)
10
farget_float = to float (target)
11
if prediction,float is None and target_ float None: target_float) /
celative_change abs (predicction
abs
14
target_float
return relative_chang <= max_relative_change
0.05
else:
return prediction lower target lover ()
Listing 1: Core evaluation code from the original ChartQA assessment method..
Furthermore, we observe that the evaluation process CharlQA in Imms-eval 94 relies on float-value comparison method, which presents several limitations in practical evaluation scenarios. The corresponding implementation shown in Listing T and an illustrative example is provided in Fig. for further analysis.
As shown Fig. for the question "What's the gap percent between the bad and the better scenarios being studied?" the intuitive answer derived from the image is 47%. And the _t______________ to_float// () function (Line Listing converts both and 47% to 0. 47, while converting the ground truth value to 47 O. Hence, the comparison at Line 14 treats both 0 47 and 47% incorrect predictions, leading
22

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to erroneous evaluation result. Moreover, when the model incorrectly predicts 46, the current evaluation method still considers correct, as the relative error compared to the ground truth 47 is:
46 47 0.02 < 0.05.
47
which is also wrong judgment result of the evaluation.
Based on this observation, all ChartQA evaluations in this paper conducted using combination of GPT-Ao-Judge and human verification, denoted as ChartQAT,
MME. The MME benchmark [5] assesses multimodal models on 14 subtasks that reflect both perceptual processing and cognitive reasoning abilities. By utilizing carefully crafted instruction- response pairs, MME aims minimize the risk of training data contamination, ensuring fair and rigorous evaluation process,
OCRBench. OCRBench 37 is comprehensive benchmark for evaluating the OCR capabilities of vision language models. It covers five key tasks: text recognition, scene text-centric VQA, document- oriented VQA, key information extraction, and handwritten mathematical expression recognition. With 29 datasets and 10,000 human-verified QA pairs across 31 scenarios. Its scenarios span street scenes, receipts, and formulas, testing models multilingual, handwritten, non-semantic, and mathematical text.
VQA. Doc [46] is dataset for VQA document images, comprising 50,000 questions defined on over 12,000 document images. It covers various document types, including forms, receipts, and scientific papers, testing models' ability understand and reason about document content, such as textual information, tables, and visual elements.
benchmark designed evaluate the real-world spatial understanding capabilities of VsMs. It consists of over 700 images, each accompanied by a question and verifiable answer, drawn from real-world scenarios, including those captured from vehicles. The benchmark assesses how well models comprehend physical environments and spatial relationships, which are crucial for applications in navigation, robotics, and general AI assistance.
RealWorldQA. RealWorldQA 774
MMVet. MMVet 992 introduces a structured framework to assess six foundational vision-language skills: recognition, OCR, knowledge, language generation, spatial awareness, and math. These capabilities are combined in 16 evaluation configurations to test how well multimodal systems can integrate them for solving complex tasks, offering detailed and quantitative performance analysis.
POPE. POPE (32] is designed to measure object hallucination in vision-language models using binary-choice questions that verify whether specific objects are present in given images. It employs metrics such as Accuracy, Recall, Precision, and FI Score across three distinct sampling strategies, delivering robust and fine-grained evaluation of hallucination tendencies. In our paper, the result of POPE is F1-score.
MMMU. MMMU 933 serves as benchmark for assessing multimodal models intricate, college- level tasks that demand both extensive knowledge and reasoning capabilities. It comprises 1 11.5K carefully selected questions sourced from exams. quizzes, and academic textbooks, spanning six broad fields: Art & Design, Business, Science, Health & Medicine, Humanities & Social Sciences, and Technology & Engineering. These questions encompass 30 academic subjects and 183 specialized areas, incorporating a wide variety of visual formats such diagrams, graphs, and chemical formulas. MMMU is designed to push models toward expert-level performance by testing their ability to understand and reason across disciplines and modalities.
benchmark for evaluating the mathematical reasoning capabilities of foundation models within visual contexts. It includes 6,141 examples, derived from 28 existing multimodal datasets involving mathematics and three newly created datasets: IQTest, FunctionQA. and PaperQA. These tasks require fine- -eee-grrindd visual understanding and compositional reasoning, often involving the interpretation of graphs, equations, and other mathematical visuals. Math Vista aims to systematically study the ability of VLMs to solve mathematical problems presented in visual formats, highlighting the need for models that can seamlessly integrate visual perception with mathematical reasoning.
Math Vista. Math Vista 42
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Math Verse. Verse [96] is benchmark for rigorously evaluating the capabilities of VLMs in interpreting and reasoning with visual information mathematical problems. Math Verse consists of 2,612 high-quality, multi-subject math problems with diagrams, each transformed into six distinct versions with varying degrees of information content in multi-modality, resulting in 15,000 test samples.
B.5 Scaling-up Reinforcement Learning on General VQA Tasks
Due to the diversity and complexity inherent in general VQA tasks, traditional rule-based reinforce- ment learning algorithms are not directly applicable. To overcome this limitation, introduce an LLM-as-Judge strategy, which enables our model to be trained via reinforcement learning on the General VQA task. To further demonstrate the effectiveness of our method, we scale up the dataset size to 130K to validate its effectiveness.
Dataset. Since the LLM-as-Judge approach is flexible, one advantage is that most of the SFT data can be utilized. Therefore, we only filter out subjective open-ended questions whose answers are not unique and can be correctly addressed from different perspectives, such as image descriptions, essay writing, and similar tasks. Based on this, we ultimately filtered 130K QA pairs to train the VLM via reinforcement learning, without requiring any cold-start phase. All the data will be open-sourced.
To verify the effectiveness of the LLM-as-Judge strategy on general VQA tasks, we conduct experiments with minimal modifications to both the system and user prompts. The detailed prompts are provided in Table
Prompt.
Reward. Since the entire training process in this setting does not involve any decision-making regarding the need for high-resolution images, the total reward function in reinforcement learning is designed to focus solely on answer quality and response formatting. Specifically, the reward comprises two components: The first component is an accuracy reward, evaluated by the LL--as- Judge. This component assesses the correctness of the model's answer against the ground truth, with a maximum of 1 point awarded for a fully correct response. The second component a formatting reward, worth 0.5 points. This is granted when the model correctly wraps its response using both the <answer><<answer>> and <think><<think>> tags. These tags are critical for maintaining consistent output formatting and enabling downstream interpretability.
Table 5: Prompt Template for VisionThink VisionThink refers to model trained general VQA tasks using full image resolution and the MLd-eeeJudge strategy. The Question placeholder is replaced with the actual question during training and inference.
SYSTEM PROMPT:
You FIRST think about the reasoning process as an internal monologue and then provide the final answer.
The reasoning process MUST BE enclosed within <think </think> tags. The final answer MUST BE put within <answer> </answer> tags. For mathematical or multiple-choice problems, wrap the answer value or choice with \boxed().
USER PROMPT:
Question.
Experimental Results. As shown in Table 6 we compare our model against state-of-the-art open- source and flosed-source vision-language models across several general VQA benchmarks. this evaluation. VisionThink denotes our model variant trained using the proposed LLM-as-Judae strategy with the above reward function and 130K QA pairs.
The experimental results demonstrate that our method outperforms the baseline model, Qwen2.5VL. In-Irutruc,, across multiple benchmarks. The improvement particularly notable on the MM where our model achieves significant performance gain of 7.9% over the baseline. This highlights the model's superior capability in handling general VQA tasks. Furthermore, on the recently popular benchmark Math Vista 42 which is designed to assess both mathematical reasoning and general visual question answering reasoning abilities, our model achieves score of 71.2. 22. This result not only surpasses all existing open-source models but also outperforms several closed-source models.
24

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Table Effectiveness of Lude Accuracy Reward Design. VisionThink model we developed by training with the LLM-as-Judge on 130K filtered General VQA datasets and leverages eval 194]
Qwen2.5-VL-77-Innste as the base model. Qwen2.5-VL-7B' reports the results evaluate by Imms-
MMMU MMMU-PPr MMBencc RealWorldQA POPE MME MathVista Verse MMVet
Method
val
test
en test
test
test
test
testmini
testmini
test
Closed-Source Model
GPT-4o 50
69.1
54.0
83.4
58.6
85.6
2329
63.8
50.2
69.1
Clande-3.5 3.5 Sonmet
2
68.3
55.0
82.6
59.9
1920
67.7
41.2
70.1
Gemini 1.5 Pro 62
62.2
49.4
73.9
70.4
88.2
63.9
64.0
Open Source General Model
Cambrain-1 1-8B 65
42.7
75.9
60.0
86.4
1803
49.0
V1.-88
81.7
84.2
LLaVA-One Vision--
48.8
66.3
88.4
1998
57.5
MiniCPM-Llan V-2.5 89
45.8
19.6
77.2
63.0
86.7
2025
54.3
MiniCPM-V.226-8B 99
49.8
27.2
78.0
65.0
83.2
2348
60.6
IXC-2.5 995
42.9
82.2
67.8
2229
63.8
51.7
IntemmV22.5888 111
56.0
38.2
84.6
70.1
90.6
2344
64.4
39.5
62.8
Reasoning Model
LLaVA-COT-IIB 78
75.0
54.8
60.3
LLaVA-Reaooer-88
07
50.6
Insight
50.2
2312
Mulberry-B
55.0
2396
63.1
Vision-RI-I RI-L.lmaV-CC--1B
2190
62.7
27.1
VisionThink
Qwen2.5 VL.-BB
3
50.3
37.7
82.6
68.6
86.7
2316
68.2
46.3
61.6
VisionThink
52.7
41.1
83.4
66.5
88.6
2314
71.2
48.3
69.5
These findings provide strong empirical evidence for the effectiveness and generalizability of our LLM-as-Judge strategy enhancing the reasoning capabilities VLMs across general VQA tasks.
B.6 Comparison with Previous Efficient VLM
To further demonstrate the effectiveness of proposed VisionThhink, we conduct comparative analysis against an additional efficient Vision-Language Model (VLM), VisionZip [80]. While previous methods such FastV [8] and Sparse [100) perform token reduction within the language model component based on attention scores, VisionZip applies reduction directly within the vision encoder using similar iitention-based mechanism.
As shown in Table 7 although previous efficient VLM methods achieve competitive performance on general VQA benchmarks, their accuracy drops significantly OCR-related tasks. This degradation is particularly evident even when a substantial portion of the visual token retained (70%), as demonstrated the ChartQA dataset.
In contrast, our proposed model, Vision Think, can smartly decide whether to request the original high-resolution image based on the complexity and demands of each sample. This adaptive strategy enables the model to maintain high accuracy on general VQA tasks while substantially improving per- formance benchmarks requiring detailed textual recognition. Through this capability, VisionThink demonstrates stronger fine-gred -nn-ggaieed visual understanding and addresses key limitation of previous efficient VLMs namely, their poor performance OCR-related tasks, which has constrained their applicability in real-world scenarios.
Notably, VisionZipi refers to the variant fine-tuned on the 130K dataset 80 However, compared to the training-free free version of VisionZip, this fine-tuned model does not show any performance improvement. We attribute this to the limited coverage and diversity of the fine-tuning dataset,
25

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Table 7: Comparison with Previous Efficient VLM Methods. Vanilla represents the Qwen2.5- VL-7B-Instreut. The retained ratio of the baseline methods is a predefined hyperparameter, while for VisionThink, the ratio is determined autonomously by the model and reported as statistical value. Note that Down-Sample refers to the model's performance when directly fed images with their resolution reduced by half. VisionZipt represents using the 130K data to finetuning the model.
ChuriQA' OCRBeecc DocVQA
MMVel RealWoridQA
Math Vista
Method
Avg.
testmini
testmini
Across Renelmoakss
81.5
2316
61.6
68.2
46.3
Vanilla
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Retain Visual Tokenz
Benchmarka
62.9
68.8
94.3
2270
54.5
68.8
82.8
62.2
43.1
Down-Sample
92.1%
78.8%
84.4%
99.1%
98.0%
88.5%
100.3%
95.5%
91.2%
93.1%
Retain 50%
Visual Tokens Across All Benchmarkks
72.6
75.8
93.6
2308
52.8
68.8
84.7
63.7
45.0
FastV (ECCV 2024)
95.8%
91.0%
93.0%
98.4%
99.6%
85.7%
100.3%
97.7%
93.4%
97.2%
73.2
75.6
66.8
2282
51,5
68.4
85.5
66.6
45.1
SparseLLM (ICML 2025)
92.2%
91.7%
92.7%
70.2%
98.5%
83.6%
99.7%
98.6%
97.6%
97.4%
73.4
70.5
93.8
2209
57.0
68.6
86.3
63.7
45.1
VisionZip (CVPR 2025)
95.0%
92.0%
86.5%
98.6%
95.4%
92.5%
100%
99.5%
93.4%
97.4%
77.3
77.9
93.8
2244
50.1
69.2
91.2
63.1
39.4
VisionZipl (CVPR 2025)
95.0%
96.9%
95.6%
98. 6%
96.9%
81.3%
100.9%
107.5%
92.5%
85.1%
Retain 70%: Visual Takeus Across All Benchmarks
77.2
82.2
94.4
2342
56.0
68.6
85,9
65.9
46.9
FastV (ECCV 2024)
98.4%
100.8%
101.1%
90.9%
99.1%
96.6%
101.3%
75.8
79.3
68.7
2276
53.7
85.4
66.3
45.1
Sparse VLM (ICML (ICML 2025)
93.6% %
72.2%
87.2%
97.2%
80.9
2334
45.8
VisionZip (CVPR 2025)
99.1 %
96.2%
99.3%
100.8%
97.4%
101. 101.0%
98.9%
2230
52.5
64.88
41.8
VisionZipt (CVPR 2025)
96.7%
99.8%
98.9%
96.3%
85.3%
106.7%
95.0%
90.3%
Retain Appvossmmtelly 51.3%
Viswol Tokens Across All Benchmarks
79.8
80.8
94.4
2400
68.5
67.1
86.0
67.5
48.0
VisionThink
101.4%
100.0%
99.1%
99.3%
103.6%
111.2%
97.8%
99.2%
99.0%
103.7%
which falls short of the supervised fine-tuning data used by the official Qwen team. This observation indirectly suggests that, compared to supervised fine-tuning, reinforcement learning provides better generalization, which we further discuss in Sec. C.I
B.7 Additional Discussion Experiments
Stronger perception tasks. We adopt the widely used CV-Bench, introduced in Cambrian-1 655 and follow its official setting and prompt for the counting task. Besides, we do not introduce any additional data for task-specific training. All models used are same those the main paper.
As shown in Table Sa both Vision and VisionThinks outperform the base model (Qwen2.5VL.
7B) the counting benchmark, demonstrating that our approach retains strong performance even on stronger visual perception tasks.
Compared to the keyword- method. Our Vision can automatically detect when the visual information insufficient and decide when to resize the image accordingly. This naturally raises an interesting question: could we leverage keyword-based detection to determine when large-sized image is needed to provide more information, and when a smaller image would be sufficient?
26

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Table 8: Additional Discussion Experiments.
(a) Results on stronger perception tasks.
(b) Comparison with Keyword-Basco method.
Task
Qwen2.5VI.-7B VisionThink VisionThink;
Task
Qwen-RL VisionThing Keyword-Ban -Based
Counting
63.1%
65.7%
67.4%
CharQQ
79.8%
79.8%
67.6%
Table 9: VisionThink results on MIMO-VL.
Mi
ChartQA
OCRBench
MME
Vanilla
91.3
86.6
2330
Down-Sample
69.8
73.1
2300
VisionThink (MiMo)
88.7
86.5
2326
Hence, to further assess the value of VisionThink, we compare it against a keyword bwsee resolution selection approach. First, we use GPT-4o to generate 100 single-word Aie-grained/OCRR-ee related keywords (e.g., counting, value, locate) and 100 short phrases (e.g., how many, fine detail). Then, the system defaults to Qwen-RL (1/4) for efficient inference. When a keyword is detected in the question, it switches to full-resolution inference via Qwen-RL. This simulates a token selection policy.
As shown in Table 8b due to the diversity in question phrasing, keyword-basse strategies generalize poorly and result in suboptimal performance. Moreover, in real-world deployment, VisionThink only requires deploying single model, while keyword- approaches require maintaining two separate models, leading to increased resource consumption.
Adding VisionThink to additional VLMs.. Recently, the MiMO team proposed MiMO-VL-SFT 75). which achieves strong performance across several benchmarks. To further demonstrate the general- ization ability of our proposed Vhiion we integrate it into MiMO-VL. As shown in Table g our method also achieves strong performance on MiMO-VL, highlighting the broad applicability and generalization capability of VisionThink.
Vision Think trained with rule-based reward easily verifiable tasks. To explore whether proposed method can be further optimized on easily verifiable tasks using rule-based approaches, we conduct additional investigations.
Datasets. We filtered structured QA samples from our training set where answers can be validated using huggingfaceemmtth-eerrr datasets, where answer verification is reliable.
or string match. These samples are primarily from OCR-related
Reward. Instead of relying on the LLM-as-Judge for binary rewards, we use rule-based verification huggingface/math-weet and string match provide the reward.
Results. As shown in Table TO. the first two models (Qwen-RL and VisionThink) are same as the main paper that trained with LLM-as- Judgee. The final column shows VisionThink (all-BBaeed) trained via rule-based reward only. It maintains strong accuracy and efficiency on verifiable tasks like ChartQA and DocVQA.
Discussion. We used the LLM-as--udgee in the main paper to handle general QA scenarios, where reliable rule-based supervision is difficult to define. However, as this experiment shows, for easily verifiable tasks such as OCR-related QA, VisionThink can effectively trained with rule-based reinforcement learning alone.
Results on more fine-grained benchmarks. In the main paper, ChartQA, OCRBench, and Math Vista are fine-grained benchmarks that typically require the model to resize images to obtain more detailed information. In contrast, benchmarks such as DocVQA, MME, Realldd, and POPE represent more general scenarios where image resizing unnecessary, as low-resolution images sufficient for the model to complete the tasks.
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Table 10: VisionThiink trained with rule-based reward.
Task
Metric
Qwen-RL
VisionThink
VisionThink (Rule-Based)
ChartQA
Accuracy
79.8
79.8
80.8
Time (8)
447
746
778
DocVQA
Accuracy
95.1
94.4
94.7
Time (s)
3076
1355
1824
OCRBench
Accuracy
81.5
80.8
79.8
Time (8)
253
211
183
Table 11: Results on more fine-grained benchmarks.
Model
V* Bench MME-RealWorld-Lic HR-Bench-4K HR-Bench-8ke TreeBench
Avg.
Resolution (WxH) 2246x 1583
2076x 1434
x023x3503
5727
2152x 1615
Token Usage
43%
110%
58.0%
51.0%
115%
Vanilla
72.3
45.1
71.4
67.6
39.5
100%
Down-Sample
69.0
39.4
69.4
66.0
37.3
94.4%
VisionThini
72.3
48.4
70.2
67.3
42.5
102.6 %
In this section, we explore the model's performance on more fine-grained benchmarks, including V* Bench, LMME-Rea/WorldLiic ERR-Bench-4K, HRR-Benc-80e and TreeBench, all of which contain high-resolution images with relatively large image sizes.
As shown in Table IT on HR-Bende Be-chn where image resolutions are extremely high, we observe that even the downsampled versions maintain sufficient visual clarity. Consequently, performance does not degrade significantly when the resolution is reduced. On MME-RealWortd-LLic and TreeBench, VisionThink only substantially outperforms the Down-Sample baseline (which uses the same input resolution), but even slightly surpasses Vanilla, which performs inference on 2x higher- resolution inputs. We hypothesize that this phenomenon occurs because, in cases where upscaling triggered, the model effectively views the same image at two different resolutions, thereby gaining a dual dual--ursppprr perspecetse understanding. This process may serve as form of implicit data augmentation, leading to improved performance. Overall, this is an exciting finding it suggests that Vision'Think only enhances efficiency in general scenarios but also has the potential improve performance on iin-nnnined tasks.
C Further Discussions
C.1
Why Use RL Instead of SFT?
In this paper, train smart and efficient vision- vision-language model via reinforcement learning. A natural question arises: why use reinforcement learning instead of supervised fine-tuning to achieve this goal?
To answer this question, we conduct comparative SFT experiment. Firstly, we construct the SFT training set. Specifically, compared to RL, which can directly utilize QA pairs and autonomously learn both the reasoning process and whether a high-resolution image is needed, SFT requires manually crafting both the reasoning steps and dialogue that involves high-resolution image requests. To overcome this limitation, we use GPT-4o to simulate both the high-resolution image requests and the corresponding reasoning process, enabling the SFT training data closely approximate the behavior of the RL-trained model. Finally, we convert the original RL training data into a format compatible with SFT, maintaining 1:1 ratio between high-resolution requests and direct answers.
As shown in Table T2 we compare the proportion of high-resotution image requests made by the SFT and RL models across evaluation benchmarks. Compared to the RL-trained model, which can smartly
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Table 12: Comparison of image resize call ratios for RL and SFT trained models multiple evaluation benchmarks.
Method
ChartQA
OCRBench DocVQA MME RealWorIdQA POPE
RI.
79.1%
62.3%
6.5%
30.7%
29.9%
9.5%
SFT
95.1%
64.0%
14.1%
39.0%
62.1%
37.8%
Table 13: Performance comparison the cold start model and no cold start model. The without cold start model represents our VisionThink.
Method
Type ChariQA OCRBench DoeVQA
MME
MMVet
RealWorldQA POPE
w/o Cold Start
RL
79.8
80.8
94.4
693/1707
68.5
67.1
86.0
Base
76.4
78.7
92.4
444/1354
58.3
47.2
86.6
Cold Start (2K)
RL
77.7
80.2
93.0
622/1624
62.3
52.8
86.2
Base
76.8
78.2
90.5
525/1368
60.5
36.5
84.8
Cold Start (8K)
RL
79.2
79.4
92.5
609/1637
66.2
55.8
85.6
decide when to answer directly and when to request a high-resolution image, the SFT model exhibits significantly higher image resize calling ratio across all benchmarks. This behavior especially evident in the RealWorI benchmark, where high-resolution images are generally unnecessary, yet the SFT model still issues requests 62.1 of the time.
Based on this observation, we find that SFT does not enable the model to become "smart" enough to accurately determine whether high-resolution image is necessary and also requires constructing the training set using GPT-4o. In contrast, RL makes the VLM smarter and more generalizable, and can directly use the original QA pairs without additional formatting.
C.2 Why not Cold-Start?
Currently, most explorations of VLMS require a cold start stage. In this section, we explore why we do not use a cold start and instead train the model directly with RI.
To investigate this problem, we collect datasets of 2K and 8K samples cold start our model. The cold-start data are constructed similarly to Sec. C.I where GPT-4o is used to simulate both the requests for high-resolution images and the corresponding reasoning processes, enabling the SFT training data to closely mimic the behavior of the model trained via RL.
We first compare the performance of the cold-started models before RL training, shown in the 'Base' lines of Table 13 Although performance improves with increasing data size, the cold-start models still fall short of the original Qwen2.5VI. We believe this is primarily due to the limited diversity and coverage of our data compared to the SFT data used by the Qwen team, resulting in the observed performance gap.
Furthermore, we compare the performance of models after RL training, using the same RL setup as VisionThink, shown in the 'RL' lines of Table 13 While RL improves the performance of cold-start models, they still fall short compared to models trained from vanilla Qwen2.5VL with RL.
Based on this observation, we conclude that due to the lower diversity and coverage of the cold-start data compared to the original Qwen2.5VL. SFT data, introducing cold-start training may improve performance in specific domains covered by the cold-start data but significantly reduces the model's general capability. This limitation restricts its broader applicability. Therefore, in this paper, we do not utilize the cold-start stage.
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C.3 Different Prompt Impact
Since we not adopt cold-start stage, designing an appropriate initial prompt becomes crucial to ensure the model begins from a good starting point. The base model Qwen2.5VL-IIstre which has not undergone RL training, typically tends to answer questions directly and lacks the ability to smartly request high-resolution images when needed.
Simple Resize
Simple Think Resize
Our
0.6
Image Resize Call Ratio
0.5
0.4
0.3
0.2
0.0
20
Step
Figure 7: Impact of Prompt Choice. Prompts lead substantial variation in image resize call ratios, with the Qwen official agent prompt demonstrating the most effective performance.
Therefore, it essential for the base model, when conditioned on our prompt, to show some preference for calling image resizing. Otherwise, if it is overly biased toward direct answering. the GRPO training process will fail to optimize effectively and may collapse into the direct-answering mode. To address this, compare three prompt settings. The first is the official agent prompt from Qwen's cookbook, shown in Table The other two are our custom-designed prompts, detailed in Table 14
Table 14: Two custom prompts for analyzing the impact of different prompts. The Question placeholder will replaced with the specific question during training and inference.
Simple Resize System Prompt:
You are a helpful assistant.
Simple Resize User Prompt:
Answer the user's question based the image provided. You can place <resize></resize> at the end of your response to call the image resize tool, will return resized image with its resolution doubled to help you better answer the question. Once you confirm your final answer, place the final answer inside <answer> and </ ansser>>. Here is the image and question: Question.
Simple Think Resize System Prompt:
You are a helpful assistant. Answer the user's question based on the image provided. You can place <resize> at the end of your response to call the image resize tool, it will return the resized image with its resolution doubled to help you better answer the question. Once you confirm your final answer, place the final answer inside <answer> and </answer>.
Simple Think Resize User Prompt:
Enclose reasoning in <think></thind Question.
30

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1.0
wio Panalty
Ponally-0.25
Ratio-0.2
Rat
Ratio-0.0
Image Resize Call Ratio
0.4
0.2
0.9
19
15
20
25
30
35
40
45
50
55
60
65
70
75
80
Step
Figure 8: Ablation Study on Penalty Ratio Threshold. As the threshold increases, the model progressively favors requesting image resizing instead of providing direct answers.
As shown in Fig. 7 our model using the official agent prompt demonstrates a higher image resize call ratio on the effective batch, which consists of equal mix of reigh-rsolutio-rrqqueee samples and direct-answer samples. In contrast, the other two custom- bestonn-esiined prompts perform poorly, both in their initial behavior and in the final trained model's ability correctly request image resizing, and ultimately collapse into consistently producing direct answers. Based on these analyses, we find that the Qwen official agent prompt, likely optimized during the pretraining or supervised fine-tuning stages by the Qwen team, is more suitable for WisionThink.
C.4 Ablation Study Penalty Control Threshold
In the main paper Eq 5 we design the penalty ratio as below:
Cdirect
Poontrol = 0.1 [Adirect[[y < <) + lnigh 2 0)]
r
(6)
Cairect + Chigh
where 0 is the threshold.
Intuitively, the larger the value of 0, the more likely the model is to penalize direct answers, thereby encouraging it to request high-resolution images. Conversely, a smaller leads the model to penalize responses that call for image resizing, thus promoting direct answers. Based this intuition, we experimented with different threshold values and recorded the proportion of high-resolution image requests within the effective batch. The results shown in Fig. 8 As indicated by the Eq. 6 increasing the threshold gradually shifts the model's behavior from favoring direct answers to favoring image resizing requests. Eventually, the model collapses into always requesting high-resolution images. However, within an appropriate range, the model's behavior not highly sensitive to the exact threshold value.
we report the performance and inference time of models trained with different penalty ratios. Overall, the trends are consistent with Fig. 8 as the penalty ratio increases, the model becomes more inclined to resize the input image, leading to higher inference time.
Besides, as shown in the Table. 15
general VQA scenarios, model performance does not significantly improve with increased inference time. However, for fine-grained understanding scenarios (e.g.. ChartQA, OCRBench), increasing the penalty ratio encourages more image resizing, which results in both longer inference time and improved performance.
31

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Table 15: Benchmark performance under different penalty ratios. Perf denotes the model performance, and Time indicates the real-world inference time.
ChartQA
OCRBench
Math Vista
MME
MMMU
RealWorldQA
Penalty
Perf
Time (s)
Perf
Time (s)
Perf
Time (s)
Perf
Time (s)
Perf
Time
Perf
Time (s)
ratio=0)
63.0
353.2
70.3
146.0
65.6
1097.1
2272
405.5
50.1
308.5
64.8
112.3
ratio=0.15
74.1
629.5
75.8
184.7
65.7
1306.5
2347
465.2
49.4
504.7
66.8
198.4
ratio=0.2
79.8
746.1
80.8
211.8
67.5
1745.5
2400
653.0
51.2
608.4
67.1
235.6
ratio=0.25
80.2
725.0
83.5
425.5
67.9
1334.6
2278
938.3
50.7
570.0
67.5
361.9
ratio=0.3
81.0
1033.4
84.8
435.6
66.6
1666.0
2235
1932.7
49.0
866.9
69.3
492.8
w/o Penalty
82.1
1093.6
85.3
437.1
67.5
1721.9
2354
1843.8
50.7
820.5
68.0
436.1
Table 16: Bias influence of LLM-as-Judge. We compare the results of several LLM models.
Model
MMMU MMMU-Pro MMBench RealWorldQA POPE
MME
Math Vista
MMVet
GPT-4o
52.7
41.1
83.4
66.5
88.6
2314
71.2
69.5
Qwen2.5-72B-Innse
52.6
40.2
84.2
66.1
88.4
2360
70.3
69.1
Bren2.5-3B-Instruue
51.9
38.5
82.6
66.9
87.7
2379
70.6
68.9
Qwen3-1.7B
51.8
38.1
82.8
67.7
87.9
2210
69.1
66.8
C.5 The Bias Influence of LLM--s-Judge
Vision employs an LLM-as-Judge Judge paradigm to assess textual answers in open-ente open-ended VQA tasks. This naturally raises question: does the inherent bias of the LLM influence the evaluation results? To mitigate potential biases, we adopt the following three design strategies:
(1) Using the LLM-as-Judge not the VLM-as- Judge. Since LIMSs generally have stronger capabil- ities than VLMs, employing an LLM reduces ballucinations and improves reliability.
(2) Filtering the dataset. We filter out subjective open-ended questions that have multiple valid answers, such as image descriptions. The remaining questions have clear ground truth, e.g., Q: <image> Who is the author of this book? A: Dewey Lambdin.
(3) Carefully designing the prompt. Our prompt requires the LLM to return discrete value: 1 for a correct answer and O for an incorrect one, instead of a continuous score. This binary format minimizes ambiguity and reduces the chance of misjudgment.
Furthermore, as shown in the Table I6 we conduct additional experiments to further investigate this issue. Specifically, we compare the judgments made by GPT-4o and Qwen2.5-72B with those from smaller models such as Qwen2.5-3B and Qwen3-1.77 While larger models achieve slightly better performance, the smallest model, Qwen3-1.7B. still achieves comparable results under our carefully designed setup. This indicates that LLM model bias has limited influence on our VisionThhie
C.6 Discussion about Zoom-in Strategy
With the release of GPT-O4, many researchers have begun focus on zoom-in strategies. Therefore. in this section, we provide a brief discussion on this topic.
Zoom-in methods rely heavily strong localization capabilities, which current models such as Qwen2.5VL-7B do not yet fully possess. Inaccurate localization may lead to suboptimal region selection, thereby degrading overall performance. Moreover, zoom-in strategies often require multiple iterative refinement steps due to imperfect region proposals, which can result in even higher total token consumption compared our approach.
Nevertheless, we acknowledge that this is a highly promising research direction and have already included it as part of our future work.
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D Qualitative Results
In this section, we present case study comparing our proposed VisionThink with other efficient VLM methods: FastV [8] SparseVLM [100), and VisionZip [80]. As shown in the three cases below, for OCR-related detail-intensive samples, our proposed VisionThink model can smartly determine when a high-resolution image is needed. In contrast to previous efficient VLMs, which suffer performance degradation due to fixed reduction ratios, VisionThink avoids such issues by making adaptive decisions based on the input, thereby maintaining strong performance.
Faull's The answr is 6.
many years
Spurse
FisionZips The &.
Think
[Tink]]
The graph shoms the ACSI (American Customer Satiefaction Indes) 70, examine
determine how for <UUprcule Image Original Resolution>
[Think]
270
D111
2P4
2307
2018
2013
013
fallowing years: 2000, 2012,2016, 2018, means the ACSI score for? years.
groph,
[Answrr]
7.
Who is the second last member in list of Members?
Faul:: N.J. MARTEL, Senior Program Officer
BAASDO
O
SparseFLM, RUTH O'BRIEN
X
VisionZip. N.J.
VisionThink:
[Think]
mumbers alphabetical lost find secead member, identify memberr and count one. <Uipscule Image Original Revelation>
[Think]
members alphabetical
"LAUREN ZEISE". Counting
memmerr, the
second last member is "CHRIS WHIPPLE."
[Answer]
chris G. Whipple
E Broader Impact Statement
This paper is conducted solely for academic research purposes. All data used in this work were collected through compliant and ethical channels, ensuring adherence to relevant data protection and usage guidelines. Furthermore, all models employed in this study comply with their respective
33

---

Fastl's No
Are there any fire hydrants here?
X
SparsekLift
PisionZipa
Fision Think:
[Thin//
The image shows a street scene with cars, buildings and some wrbon inft infrestucturr To determise if there are fire hydrants, I'll need to carefully wamins the image aor visible fire bytrannts. <Uppscale Image
Revoletionz
[Think]
resized right imago,
shape typical fire hydrants.
[Anwery]
Yes
license agreements. As such, this research upholds high standards of integrity and responsibility, with no foreseeable negative societal impact.
34

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NeurIPS Paper Checklist
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Reviews:
[{"review_id": "8EyftWkKSa", "reviewer": "Reviewer_5eUF", "summary": "This paper proposes a method, VisionThink, which improves the reasoning ability and efficiency of LVLMs in VQA tasks by using multi-round GRPO and adaptive adjustments to the input image resolution.", "questions": "1. Can you provide a more detailed discussion of the resolution issue?  Could you also compare the results when resizing the input image resolution during testing to match that of training?\n\n2. The VQA task appears to be less sensitive to visual perception. Could you include tasks that rely more heavily on visual perception abilities, such as grounding and counting, to better demonstrate the effectiveness of VisionThink?\n\n3. In tasks like DocVQA, VisionThink does not show clear advantages over Qwen-RL (1/4) in terms of performance and efficiency. On the other hand, in tasks requiring image resizing like ChartQA, VisionThink adds significant inference time without improving performance. Could you discuss whether it might be more beneficial to select Qwen-RL or Qwen-RL (1/4) based on the specific task type?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\nThe experiments are sufficient, the motivation is clear, and there is a performance improvement in VQA tasks, along with increased efficiency in some tasks. The paper is well-written and easy to read.\n\nWeaknesses:\n\n1). The discussion on the resolution issue is not sufficient. While I agree that this is task-dependent, I believe the performance drop in the VQA task due to the direct use of RL training might be caused by the gap between the input image resolution in training and testing. I suggest the authors add more discussion and comparisons, including resizing the input image resolution during testing to match that of training and comparing it with VisionThink.\n\n2). The VQA task is not very sensitive to visual perception. It would be helpful to include tasks that require stronger visual perception abilities, such as grounding and counting.\n\n3). In tasks that do not require significant image resizing, such as DocVQA, VisionThink shows no obvious performance or efficiency advantage over Qwen-RL (1/4). On the other hand, in tasks that require image resizing, such as ChartQA, VisionThink does not improve performance but introduces significant additional inference time. This raises concerns about the value of the method. Would it be better to choose Qwen-RL or Qwen-RL (1/4) depending on the task type?", "was_revised": true, "final_justification": "The authors have addressed all of my concerns, so I will raise my score to 4. However, I still have a lingering worry that VisionThink relies solely on Qwen and lacks experiments on other MLLMs, which makes it difficult to demonstrate sufficient generalizability and robustness at this stage.", "rebuttal": "Dear Reviewer 5eUF,\n\nMany thanks for your constructive and insightful feedback! We're glad you found the **motivation clear**, the **experiments sufficient**, and the paper **easy to read**.  We have revised the paper following your suggestions, and address your questions below.\n\n---\n> ### [Q1.1 / W1] More detailed discussion on the resolution issue.\n\nIn Figure 1, image resolution is the only controlled variable. Specifically, we resize the image resolution to reduce the number of visual tokens and evaluate the performance of the official Qwen2.5-VL on several benchmarks. The results show that in most general VQA scenarios, even reducing tokens by 75% has minimal impact on the model’s performance. However, in scenarios requiring detailed understanding and OCR-related capabilities, reducing the number of visual tokens leads to a significant drop in performance.\nThis observation suggests that significant token redundancy exists in most scenarios, but a uniform token reduction ratio should not be applied across all tasks.\n\nThank you for your kind suggestion; we will include the detailed discussion in Paragraph 3, Lines 33–43.\n\n\n> ### [Q1.2 / W1] Results comparison for same resolution during training and testing.\n\nIn Figure 1, which shows the motivation of our paper, the model used is Qwen2.5-VL, which we did not train. For a fair comparison, the results reported in Figure 4, Qwen-RL and Qwen-RL (1/4)—are based on models trained and tested at the **same corresponding resolutions**. In other words, the training and testing resolutions are **consistent**.\n\nThank you for your comments. We will add further explanation in the paper to improve clarity.\n\n> ### [Q2 / W2] VisionThink on stronger visual perception tasks.\n\nThank you for your insightful suggestion. To evaluate VisionThink on tasks requiring stronger visual perception, we include an additional **counting benchmark** in our analysis.\n\n**Settings.**\nWe adopt the widely used **CV-Bench**, introduced in Cambrian-1 \\[34], and follow its official setting and prompt for the counting task.\n\n**Models.**\nWe do not introduce any additional data for task-specific training. All models used are same to those in the main paper. Here, VisionThink‡ denotes the version trained on general VQA tasks with full-resolution inputs using the LLM-as-Judge strategy.\n\n**Results.**\nAs shown below, both VisionThink and VisionThink‡ outperform the base model (Qwen2.5VL-7B) on the counting benchmark, demonstrating that our approach retains strong performance even on stronger visual perception tasks:\n\n| Task     | Qwen2.5VL-7B | VisionThink | VisionThink‡ |\n| -------- | ------------ | ----------- | --------------------- |\n| Counting | 63.1         | 65.7        | 67.4                  |\n\nWe will include this result in the final version of the paper to better reflect the generalization of our method. Thank you again for the valuable suggestion.\n\n\n\n> ### [Q3 / W3] Value of VisionThink compared with choose Qwen-RL or Qwen-RL (1/4) depending on the task type.\n\nThanks for your constructive question, we want to claim our method value in three aspects:\n\n(1) **Balanced Efficiency and Effectiveness** \n    \n* Qwen-RL can ensure strong performance, but it consumes a significant amount of time even on simple tasks. \n* Qwen-RL (1/4) significantly reduces the time cost, but its performance drops sharply on tasks that require detailed understanding, such as OCR-related tasks.\n* Our VisionThink achieves performance comparable to Qwen-RL while drastically reducing time consumption—by up to 2×. Compared to Qwen-RL (1/4), VisionThink delivers significantly better performance with similar time efficiency.\n\n(2) **Unpredictable Task Types** \n* In real-world deployment scenarios, we cannot predict in advance what specific task type the user **per-sample input** belongs to. \n* Whether a full-resolution image is needed depends jointly on both the image content and the question type. For example, as shown in Supplementary Section D (Qualitative Results), even for the same image, different questions may require different numbers of image tokens.\n* Our VisionThink model is end-to-end and can automatically determine whether more image tokens are needed or if 1/4 of the tokens are sufficient, thereby minimizing image token usage while maintaining performance.\n\n(3) **Research Direction**\n* We encourage the community to move beyond the pursuit of traditional Efficient VLMs and more focus on: Efficient Reasoning VLMs.\n\n\nTo further assess the value of VisionThink, we compare it against a keyword-based resolution selection approach.\n\n**Keyword Construction.**\nWe use GPT-4o to generate 100 single-word fine-grained/OCR-related keywords (e.g., *counting*, *value*, *locate*) and 100 short phrases (e.g., *how many*, *fine detail*).\n\n**Setup.**\nThe system defaults to `Qwen-RL (1/4)` for efficient inference. When a keyword is detected in the question, it switches to full-resolution inference via `Qwen-RL`. This simulates a keyword-triggered token selection policy.\n\n**Results.**\nAs shown below, due to the diversity in question phrasing, keyword-based strategies generalize poorly and result in suboptimal performance.\nMoreover, in real-world deployment, **VisionThink only requires deploying a single model**, while keyword-based approaches require maintaining **two separate models**, leading to increased resource consumption.\n\n| Task    | Qwen-RL | Qwen-RL (1/4) | VisionThink | Keyword-Based |\n| ------- | ------- | ------------- | ----------- | ------------- |\n| ChartQA | 79.8    | 62.9          | 79.8        | 67.6          |\n\nWe will emphasize this discussion in the camera-ready version to better illustrate the advantages of VisionThink, which we believe will further clarify the contributions of our paper. Thank you again for your suggestion.\n\n\n\n[34] Tong P, Brown E, Wu P, et al. Cambrian-1: A fully open, vision-centric exploration of multimodal llms[J]. Advances in Neural Information Processing Systems, 2024, 37: 87310-87356.\n\n---\n**Thank you again for helping us improve the paper and hope our response can resolve your concerns! Please let us know if you have any further questions. We will be actively available until the end of rebuttal period. If you feel your concerns are addressed, please consider reevaluating our work. Looking forward to hearing from you :-) !**"}, {"review_id": "86YO4qyuCw", "reviewer": "Reviewer_wk5x", "summary": "This paper introduces VisionThink, a paradigm for efficient vision-language modeling that dynamically adapts image resolution on a per-sample basis. Rather than uniformly processing all inputs at full resolution or applying fixed token pruning, VisionThink begins inference with a downsampled image (1/4 resolution) and uses a learned policy—trained via reinforcement learning—to decide whether the current low-resolution input suffices or if the high-resolution image should be requested. Experiments on nine VQA benchmarks (e.g., ChartQA, OCRBench, DocVQA) demonstrate that VisionThink matches or exceeds state-of-the-art accuracy while reducing average visual token usage and achieving up to 2× speed-ups on non-OCR tasks.", "questions": "1. Could the authors perform ablation studies to examine how using different LLMs as critics affects the experimental results, and whether this helps mitigate potential biases introduced by relying on a single model?\n\n2. For tasks where ground-truth answers are easily verifiable (e.g., math reasoning, structured QA), is the use of LLM-as-Judge necessary? Have the authors considered training VisionThink on such datasets purely using exact-match or programmatic checking as the reward signal? It would be helpful to see experiments or discussions demonstrating whether VisionThink’s benefits persist without the additional complexity of an LLM-as-Judge.", "limitations": "See weaknesses above.", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n1. The topic focus on in this paper is very important in real-world scenarios. Introducing per-sample, decision-driven image resizing is very different from a fixed pruning strategy and addresses different redundancies between images and questions. For me, this direction and the proposed method have high potential and are very interesting.\n\n2. When reducing the image token, VisionThink maintains a performance close to or even higher than SoTA on multiple benchmarks.\n\nWeaknesses:\n\n1. VisionThink employs an LLM-as-Judge to assess textual answers in open-ended VQA tasks. While this is innovative, it introduces a significant risk: the evaluation standard fully depends on the judgments of the LLM, which itself is prone to inherent biases, hallucinations, or domain-specific knowledge gaps. For instance, if the judge LLM has insufficient understanding of terminology related to minority groups or specialized fields, it may produce incorrect judgments, potentially leading VisionThink’s policy learning to converge in the wrong direction.\n\n2. A significant concern is that the paper does not specify the sources, composition, or any statistics of the training datasets used for VisionThink. This omission poses challenges for reproducing the results and assessing the validity of the conclusions, as the choice of training data can heavily influence model performance and generalization. Inappropriate or overlapping training data might introduce data leakage, thereby compromising the reliability of the evaluation and potentially inflating the reported gains.", "was_revised": true, "final_justification": "I sincerely appreciate the authors' detailed response. The rebuttal has thoroughly addressed all of my concerns. I believe this work makes a meaningful contribution to advancing the real-world applicability of vision-language models. Accordingly, I have raised my score to 6. I also encourage the other reviewers to re-evaluate the practical significance of this work and consider adjusting their scores. I strongly recommend to accept this paper.", "rebuttal": "Dear Reviewer wk5x:\n\nMany thanks for your constructive and insightful feedback! We are glad that you found this paper's **topic is very important** and our **method has high potential**. We have revised the paper following your suggestions, and will address your questions below.\n\n---\n> ### [Q1 / W1] Bias Influence of LLM-as-Judge\n\nYour question is very insightful, and we also share concerns about potential biases in the reward model. To mitigate this, we adopt three specific design choices:\n\n**(1) Using the LLM-as-Judge not the VLM-as-Judge.** Since LLMs generally have stronger capabilities than VLMs, employing an LLM reduces hallucinations and improves reliability.\n\n\n**(2) Filtering the dataset.** We filter out subjective open-ended questions that have multiple valid answers, such as image descriptions (Appendix. Line 202–204). The remaining questions have clear ground truth, e.g.,\nQ: *\\<image\\>* Who is the author of this book?\nA: Dewey Lambdin.\n\n**(3) Carefully designing the prompt.** Our prompt requires the LLM to return a discrete value: 1 for a correct answer and 0 for an incorrect one, instead of a continuous score. This binary format minimizes ambiguity and reduces the chance of misjudgment (Appendix. Sec. B.1.1).\n\nFurthermore, as shown in the table below, we conduct **additional experiments** to further investigate this issue. Specifically, we compare the judgments made by `GPT-4o` and `Qwen2.5-72B` with those from smaller models such as `Qwen2.5-3B` and `Qwen3-1.7B`. While larger models achieve slightly better performance, the smallest model, `Qwen3-1.7B`, still achieves comparable results under our carefully designed setup. This indicates that LLM model bias has limited influence on our VisionThink.\n\n| Model                | MMMU | MMMU-Pro | MMBench | RealWorldQA | POPE | MME  | MathVista | MMVet |\n| -------------------- | ----- | -------- | ------- | ----------- | ---- | ---- | --------- | ----- |\n| GPT-4o               | 52.7  | 41.1     | 83.4    | 66.5        | 88.6 | 2314 | 71.2      | 69.5  |\n| Qwen2.5-72B-Instruct | 52.6  | 40.2     | 84.2    | 66.1        | 88.4 | 2360 | 70.3      | 69.1  |\n| Qwen2.5-3B-Instruct  | 51.9  | 38.5     | 82.6    | 66.9        | 87.7 | 2379 | 70.6      | 68.9  |\n| Qwen3-1.7B           | 51.8  | 38.1     | 82.8    | 67.7        | 87.9 | 2210 | 69.1      | 66.8  |\n\nThanks again for your professional suggestion, we will include a new subsection titled **\"The Bias Influence of LLM-as-Judge\"** in Sec. C (Further Discussions), and provide additional details in Sec. B.1.1 on the prompt design.\n\n\n> ### [W2] Concern of the dataset source and composition\n\nOur datasets are primarily filtered from open-source data, including the LLaVA-OneVision and Cambrian-1 datasets. All data used in our work are publicly available. Furthermore, we commit to releasing our training and validation datasets, along with the complete codebase, in our GitHub repository for full transparency and reproducibility. We welcome continued community oversight.\n\n\n\n\n> ### [Q2] VisionThink trained with rule-based reward on easily verifiable tasks\n\nThank you for the valuable suggestion. Following your advice, we trained **VisionThink** using a **rule-based reward** signal on tasks where answers can be easily verified.\n\n**Datasets.**\nWe filtered structured QA samples from our training set where answers can be validated using `huggingface/math-verify` or  `string match`. These samples are primarily from **OCR-related datasets**, where answer verification is reliable.\n\n**Reward.**\nInstead of relying on the LLM-as-Judge for binary rewards (as in the main paper), we use rule-based verification `huggingface/math-verify` and `string match` to provide the reward.\n\n**Results.**\nAs shown below, the first three models (Qwen-RL, Qwen-RL (1/4), and VisionThink) are same as the main paper that trained with LLM-as-Judge. The final column shows **VisionThink (Rule-Based)** trained via rule-based reward only. It maintains strong accuracy and efficiency on verifiable tasks like ChartQA and DocVQA.\n\n\n| Task     | Metric     | Qwen-RL | Qwen-RL (1/4) | VisionThink | VisionThink (Rule-Based) |\n|----------|------------|---------|----------------|-------------|---------------------------|\n| ChartQA  | Accuracy   | 79.8    | 62.9           | 79.8        | 80.8                      |\n|          | Time (s)   | 447     | 341            | 746         | 778                       |\n| DocVQA   | Accuracy   | 95.1    | 94.3           | 94.4        | 94.7                      |\n|          |  Time (s)  | 3076    | 1327           | 1355        | 1824                      |\n| OCRBench | Accuracy   | 81.5    | 68.8           | 80.8        | 79.8                      |\n|          |  Time (s)  | 253     | 132            | 211         | 183                       |\n\n**Discussion.**\nWe used the **LLM-as-Judge** in the main paper to handle **general QA scenarios**, where reliable rule-based supervision is difficult to define. However, as this experiment shows, for **easily verifiable tasks** such as OCR-related QA, VisionThink can be effectively trained with rule-based reinforcement learning alone.\n\nThank you again for the professional suggestion. We will include this experiment in the main paper and provide additional details in *Section B.7*. \n\n---\n**Thank you again for helping us improve the paper and hope our response can resolve your concerns! Please let us know if you have any further questions. We will be actively available until the end of rebuttal period. If you feel your concerns are addressed, please consider reevaluating our work. Looking forward to hearing from you :-) !**"}, {"review_id": "HXXGNfFYjP", "reviewer": "Reviewer_2Thu", "summary": "This paper proposes VisionThink, a reinforcement learning (RL)-based framework that enables vision-language models (VLMs) to autonomously determine whether high-resolution visual input is required to answer visual question answering (VQA) tasks. To achieve this, the authors employed Qwen2.5-VL-7B to curate 10K samples requiring 2× higher resolution images for accurate answers, alongside another 10K samples where quarter-resolution images sufficed for correct responses. These curated datasets were then utilized for RL training. Experimental results demonstrate VisionThink's efficiency in adaptive resolution selection while maintaining strong performance in VQA tasks.", "questions": "N/A", "limitations": "Yes, the authors have discussed limitations in the manuscript.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n1. The motivation is clear and quite reasonable.\n2. The proposed penalty is novel and effective.\n3. Regarding resizing as a tool and then leveraging the agent prompt is interesting and effective.\n\n\n**Weaknesses**\n1. Evaluations are not sufficient. In Table 2, only the averaged number of visual tokens across all benchmarks is reported. However, it is crucial to report the number of visual tokens for each benchmark SEPARATELY, as well as the mean input resolution.\n2. By going deeper with Table 2, the performance improvements mainly come from MMVet and MathVerse, and these two benchmarks are widely known to be reasoning-related. Therefore, the improvements mainly come from the reasoning ability, rather than the proposed ability to \"request a larger image.\" Therefore, it is strongly encouraged to remove these two benchmarks or replace them with benchmarks that particularly require larger images. Furthermore, the vanilla text RL baseline should be incorporated.\n3. The computation of $C$ in Eq. (5) is unclear. Are they computed online? A flowchart of pseudo-code is really appreciated.\n4. The data source is unclear. A specific data source should be provided.", "was_revised": true, "final_justification": "I really appreciate the author's effort, especially regarding my W1 and W2. Therefore, I am raising my rating to 4. The authors should include these additional experiments and discussions in their revised version.", "rebuttal": "Dear Reviewer 2Thu:\n\nMany thanks for your constructive and insightful feedback! We are glad that you found our VisionThink's **motivation is clear and reasonable**. We have revised the paper following your suggestions, and will address your questions below.\n\n---\n> ### [W1 & W2] Table present suggestions\n\nThank you sincerely for your thoughtful and professional suggestions, we think they significantly improved the clarity of our table presentation.\n\nFollowing your advice, we revised Table 2 as follows:\n\n* We **split the benchmarks** into *fine-grained* and *general-scenario* categories.\n* We **removed MMVet and MathVerse** as suggested.\n* We added **mean resolution** and **token usage** per group.\n* We included **VisionThink‡**, a text-only RL baseline trained with full-resolution images using vanilla GRPO and LLM-as-Judge reward.\n\nThe revised tables are shown below (Markdown formatting limitations acknowledged):\n\n---\n\n### **Fine-Grained Benchmarks**\n\n| Method                         | ChartQA | OCRBench | MathVista | Avg.        |\n| ------------------------------ | ------- | -------- | --------- | ----------- |\n| Mean Resolution                | 583×768 | 732×615  | 439×584   |             |\n| Token Usage                    | 104%    | 87.3%    | 102%      |             |\n| Vanilla                        | 79.8    | 81.5     | 68.2      | 100%        |\n| Down-Sample                    | 62.9    | 68.8     | 62.2      | 84.8%       |\n| SparseVLM (ICML 2025)          | 73.2    | 75.6     | 66.6      | 94.0%       |\n| FastV (ECCV 2024)              | 72.6    | 75.8     | 63.7      | 92.5%       |\n| VisionThink‡ (Vanilla Text RL) | 81.4    | 83.4     | 71.2      | **102.1%**  |\n| **VisionThink**                | 79.8    | 80.8     | 67.5      | **`99.4%`** |\n\n---\n\n### **General-Scenario Benchmarks**\n\n| Method                         | DocVQA    | MME      | RealWorldQA | POPE    | Avg.         |\n| ------------------------------ | --------- | -------- | ----------- | ------- | ------------ |\n| Mean Resolution                | 2099×1783 | 945×1086 | 1030×1316   | 480×585 |              |\n| Token Usage                    | 31.5%     | 45.7%    | 54.9%       | 34.6%   |              |\n| Vanilla                        | 95.1      | 2316     | 68.6        | 86.7    | 100%         |\n| Down-Sample                    | 94.3      | 2270     | 68.8        | 82.8    | 98.2%        |\n| SparseVLM (ICML 2025)          | 66.8      | 2282     | 68.4        | 85.5    | 91.8%        |\n| FastV (ECCV 2024)              | 93.6      | 2308     | 68.8        | 84.7    | 99.0%        |\n| VisionThink‡ (Vanilla Text RL) | 95.1      | 2307     | 68.6        | 87.9    | **100.2%**   |\n| **VisionThink**                | 94.4      | 2400     | 67.1        | 86.0    | **`100.0%`** |\n\n---\n\nAs shown in the *Fine-Grained Benchmarks*, **VisionThink outperforms prior efficient VLMs** by autonomously requesting high-resolution images when necessary. In the *General-Scenario Benchmarks*, it uses **fewer visual tokens** while achieving performance comparable to the full-resolution **VisionThink‡** baseline. We believe this more clearly demonstrates the effectiveness of our method.\n\nThank you again for your valuable suggestions. We will further improve the tables in the camera-ready version.\n\n> ### [W3] The Computation of $C_{direct}$ and $C_{high}$ in Eq.5.\n\nThe computation of $C_{direct}$ and $C_{high}$ is online. Specifically, $C_{direct}$ and $C_{high}$ represent the number of correct answers from the low- and high-resolution inputs, respectively.\n\nWe provide the following pseudo-code for clarity. In actual implementation, the for loop is avoided and replaced with vectorized operations for efficiency.\n\n\n```pseudo\n# Algorithm: Computation of C via VisionThink Inference\n\n# Inputs:\n#   inputs             : input data for VisionThink\n#   visionthink_model  : model with .inference(inputs) → (outputs, rounds)\n#   reward_model       : function outputs → rewards ∈ {0, 1}\n\n# Outputs:\n#   C_direct : count of correct answers from low-resolution pass (round == 1)\n#   C_high   : count of correct answers from high-resolution pass (round == 2)\n\n# Step 1: Perform model inference\noutputs, round_numbers = visionthink_model.inference(inputs)  # rounds ∈ {1, 2}\n\n# Step 2: Compute binary rewards\nrewards = reward_model(outputs)  # Shape: (N,), rewards ∈ {0, 1}\n\n# Step 3: Count reward-aligned responses per resolution\nC_direct = 0\nC_high = 0\n\nfor i in range(len(rewards)):\n    if rewards[i] == 1 and round_numbers[i] == 1: # correct answers from low‑resolution pass\n        C_direct += 1\n    elif rewards[i] == 1 and round_numbers[i] == 2: # correct answers from high‑resolution pass \n        C_high += 1\n```\n\n> ### [W4] Data source clarification.\n\nOur datasets are primarily filtered from open-source data, including the LLaVA-OneVision and Cambrian-1 datasets. All data used in our work are publicly available. Furthermore, we commit to releasing our training and validation datasets, along with the complete codebase, in our GitHub repository for full transparency and reproducibility. We welcome continued community oversight.\n\n\n---\n**Thank you again for helping us improve the paper and hope our response can resolve your concerns! Please let us know if you have any further questions. We will be actively available until the end of rebuttal period. If you feel your concerns are addressed, please consider reevaluating our work. Looking forward to hearing from you :-) !**"}, {"review_id": "6Ub07Gibtl", "reviewer": "Reviewer_maK1", "summary": "This paper introduce a vision-language model that can adaptively determines the number of image tokens. At inference time, the model initially processes a low-resolution (1/4-scale) version of the image along with the input question. During autoregressive decoding, it dynamically decides whether higher-resolution visual information is required, and if so, retrieves additional visual tokens from the original full-resolution image.  The authors fine-tune a Qwen VL baseline using the GRPO reinforcement learning algorithm on a dataset of 20K samples. To guide the training, a reward function is designed that combines answer accuracy, evaluated using an LLM as judge, with a penalty term that discourages excessive use of high resolution inputs. Empirical results demonstrate that the model achieves a 1.4% performance improvement over the baseline while utilizing only 51.3% of the image tokens.", "questions": "Please address the concerns raised in the weaknesses section, particularly  \n- 1.1–Line87,\n- 1.2–Line46,\n- 1.2–Line121,\n- weakness2, \n- weakness3.", "limitations": "The paper presents a novel approach that leverages reinforcement learning to adaptively decide whether additional visual tokens are needed, which is conceptually appealing. However, the method introduces complexity and lacks a clear research motivation and comparison with simpler alternatives, as discussed in Weaknesses2. Moreover, clear and correct presentation is especially important for a top-tier conference like NeurIPS. As noted in Weaknesses1, the paper exhibits numerous issues in clarity and correctness that may fall short of the standards expected for a NeurIPS submission. As such, my current recommendation leans toward rejection.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 1, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "## Strengths\n1. The proposed methodology that enables the model to autonomously decide the number of image tokens needed is novel.\n2. Applying reinforcement learning to vision-language models (VLMs) in order to induce specific behaviors is still relatively uncommon, making this attempt valuable.\n3. The well-structured figures and tables contribute positively to the paper’s clarity.\n\n## Weaknesses\n1. With further refinement, especially in the following two aspects, the overall presentation could be significantly improved.\n   - (**1.1\\***) Certain elements are confusing and should be corrected (e.g., Line 13, Line 27, Line 87).  \n   - (**1.2\\***) Several aspects of the writing require polishing for clarity and professionalism (e.g., Line 28, Line 46, Line 121, Figure 3, Line 149, Figure 4, Table 2).\n\n2. While the proposed approach is novel, its efficiency and effectiveness would benefit from further validation. For example, VisionZip [58 in the supplementary material] shows that using only 33% or even 25% of visual tokens retains 99.1% and 98.4% of baseline performance, respectively. In contrast, VisionThink sometimes uses more visual tokens than the baseline (i.e., 25% + 100%), and as shown in Figure 4, it results in longer inference time on tasks like ChartQA.\n   This raises a concern: if a model were to simply (supervised) fine-tune the baseline model with their 20K dataset and apply a rule-based strategy (e.g., use the original resolution whenever the question contains OCR- or ChartQA-related keywords, and use 1/4 resolution otherwise), it might achieve similar performance improvement, without needing such a complex RL setup.   \n\n3. A deeper analysis of the proposed method, beyond focusing solely on performance gains, would strengthen the paper and help alleviate the previous concerns. For example, analyzing which image-question pairs triggered high-resolution requests in Figure 5 would offer useful insight. It would also be interesting to know whether the Special Token for Image Upscaling can be effectively fine-tuned in QwenVL.\n\n\n### **1.1\\*** \n   - Line13: The phrase “whether to compress tokens” is misleading. VisionThink does not actually **compress** tokens; rather, it decides whether to incorporate additional tokens from a higher-resolution image, thereby contributing to a **reduction** in the overall use of visual tokens.\n   - Line27: The claim that `the consumption of visual tokens increases exponentially since LLaVA-1.5 used 576 visual tokens but Qwen-VL-2.5 requires 2678` may cause confusion. The facts are more nuanced: (1) LLaVA uses 576 tokens because it forcibly resizes input images to 336×336; (2) the released Qwen-VL model, when inferring on a 336×336 image, actually uses fewer tokens (around 144); and (3) Qwen-VL uses more tokens only when processing high-resolution images like 2048×1024. This complexity is not reflected in the current explanation.\n   - Line87: While the authors discuss the sequence length n, the impact of the number of output tokens is overlooked, which can significantly influence both training and inference cost. For example, LLaVA’s visual tokens number 576, but its total max token length is [2048](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune.sh); Qwen-VL’s visual tokens range from 114 to 2678, yet its total max token length can go up to [8192](https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-finetune/scripts/sft_7b.sh). In practice, output token length may have even greater computational impact than the number of image tokens.\n\n### **1.2\\*** \n   - Line28: The motivation for reducing visual tokens should be better articulated. Instead of saying \"Qwen-VL uses more tokens than LLaVA, so we should reduce them,\" a more compelling argument would emphasize the memory constraints on real-world devices.\n   - Line46: It would be helpful to provide some explanation for the method's name, VisionThink. As it stands, the name does not clearly reflect the methodology. Something like AdaVis-VLM (Adaptive + Visual) might have been more intuitive.\n   - Line121: It is unclear whether the 130K dataset is used to train the model via LLM-as-Judge, or merely to evaluate model outputs. This should be clarified.\n   - Figure3: The caption lacks sufficient detail, especially compared to other figures and tables. For consistency, a more descriptive caption would be helpful.\n   - Line149: Much of the content in Section 3.2 would be better placed here to improve flow and cohesion.\n   - Figure4: It is unclear whether the inference time cost is reported in seconds or milliseconds. This should be specified.\n   - Table2: Including citations for the baseline methods would greatly aid readers.", "was_revised": true, "final_justification": "I appreciate the authors’ thorough rebuttal. Therefore, I am raising my rating to 4. I hope the authors will make an effort to address the remaining concerns regarding points 4 and 5 in *Official Comment by Reviewer maK1*.", "rebuttal": "Dear Reviewer maK1,\n\nMany thanks for your constructive and responsible feedback! We are glad that you found our VisionThink is **novel and valuable**. We have revised the paper following your suggestions, and will address your questions below first and then address other weaknesses.\n\n---\n## Questions\n> ### [Q1] 1.1 Line87: Overlooked the number of output tokens.\n\nThank you for the insightful comment. We agree that inference time generally consists of two components: **prefilling** and **decoding**, and the number of output tokens impacts the latter. However, we would like to clarify the following:\n\n(1) In contrast to LLMs, **VLMs typically generate much shorter textual outputs**, often only tens to a hundred tokens. This is significantly smaller by **orders of magnitude** compared to the number of visual input tokens (which can reach hundreds or thousands). As a result, **prefill time dominates total inference time** in most VLM scenarios.\n\n(2) Moreover, the inference time reported in our paper is **measured in real-world environments**, reflecting actual efficiency. As shown in *Figure 4*, our method achieves speed improvements across multiple benchmarks, highlighting its practical advantage.\n\n(3) We appreciate your insightful suggestion and will incorporate a detailed discussion of this issue in *Section 2.2 Computation Complexity* to clarify the relative contribution of output token length and better support our claims.\n\n\n\n> ### [Q2] Line 46: Explanation for the method name ``VisionThink``\n\nThank you for the suggestion. Our method is the *“think with images”* paradigm, where the model first reasons over a coarse visual input and then decides, via reinforcement learning, whether higher-resolution visual information is necessary.  Besides, the RL method is also widely used to strengthen the LLM's thinking ability.\nTherefore, we name our approach **VisionThink** to emphasize this *think with images* mechanism.\n\nWe appreciate your kind comment and will revise the manuscript to include a clearer explanation, such as:\n\n> *“We name our approach VisionThink to highlight its think‑then‑look‑clearer paradigm: the policy reasons over a coarse view before deciding whether higher‑resolution evidence is necessary.”*\n\n> ### [Q3] 1.2–Line121: Clarification the 130K Dataset. \n\nThanks for pointing this, and we will detail more in our manuscript. All the datasets are utilized to trained our model via reinforcement learning (Main paper Line122 & Appendix B.5 Line201-205). And we commit to open-source our training and validation datasets, along with the complete codebase, in our GitHub repository.\n\n> ### [Q4.1] Weakness 2.1: Comparison with VisonZip. \n\nThanks for your interest in VisionZip, you mentioned 99.1% and 98.4% performance retention rates were obtained on **LLaVA-1.5**. When applying the official `github/VisionZip/Qwen_2_5VL` procedure to **Qwen-2.5-VL**, performance drops significantly. As shown in *Appendix Table 5*, with 50% token retention, VisionZip achieves only **92.0% on ChartQA** and **86.5% on OCRBench**. In contrast, on these fine-grained benchmarks, our **VisionThink** maintains **100%** and **99.1%**, respectively.\n\n *Appendix. Table 5: **Comparison with Previous Efficient VLM Methods.** VisionZip‡ represents the SFT finetuned model.*\n> | Method       | ChartQA | OCRBench | DocVQA | MME  | MMVet | RealWorldQA | POPE | MathVista | MathVerse | Avg.  |\n> |------|--|-----|----|------|--------|-------|------|-----|---------|-----|\n> | Vanilla  | 79.8    | 81.5     | 95.1   | 2316 | 61.6   | 68.6         | 86.7 | 68.2       | 46.3       | 100%   |\n> | (%)| 100%    | 100%     | 100%   | 100% | 100%   | 100%         | 100% | 100%       | 100%       |        |\n> | Down-Sample  | 62.9    | 68.8     | 94.3   | 2270 | 54.5   | 68.8         | 82.8 | 62.2       | 43.1       | 92.1%  |\n> | (%) | 78.8%   | 84.4%    | 99.1%  | 98.0%| 88.5%  | 100.3%       | 95.5%| 91.2%      | 93.1%      |        |\n> | FastV [7] (ECCV 2024)| 72.6    | 75.8     | 93.6   | 2308 | 52.8   | 68.8         | 84.7 | 63.7       | 45.0       | 95.8%  |\n> | (%) | 91.0%   | 93.0%    | 98.4%  | 99.6%| 85.7%  | 100.3%       | 97.7%| 93.4%      | 97.2%      |        |\n> | SparseVLM [72] (ICML 2025)       | 73.2    | 75.6     | 66.8   | 2282 | 51.5   | 68.4         | 85.5 | 66.6       | 45.1       | 92.2%  |\n> | (%) | 91.7%   | 92.7%    | 70.2%  | 98.5%| 83.6%  | 99.7%        | 98.6%| 97.6%      | 97.4%      |        |\n> | VisionZip [58] (CVPR 2025)       | 73.4    | 70.5     | 93.8   | 2209 | 57.0   | 68.6         | 86.3 | 63.7       | 45.1       | 95.0%  |\n> | (%)   | 92.0%   | 86.5%    | 98.6%  | 95.4%| 92.5%  | 100%         | 99.5%| 93.4%      | 97.4% |        |\n> | VisionZip‡ [58] (CVPR 2025)    | 77.3  | 70.3   | 93.8  | 2244 | 50.1   | 69.2         | 91.2 | 63.1       | 39.4       | 95.0%  |\n> | (%)  | 96.9%   | 95.6%    | 98.7%  | 96.3%| 81.3%  | 100.9%      | 107.5%| 92.5%     | 85.1%      |        |\n> | VisionThink  | 79.8    | 80.8     | 94.4   | 2400 | 68.5   | 67.1 | 86.0 | 67.5       | 48.0       | **101.4%** |\n> | (%)  | 100.0%  | 99.1%    | 99.3%  | 103.6%| 111.2% | 97.8%| 99.2%| 99.0%  | 103.7%     |  |\n\n\n\n\n> ### [Q4.2] Weakness 2.2: Comparison with SFT\n\nThank you for this professional question. We have also carefully considered this issue and discussed it in **Appendix B.6** and **Appendix C**.\n\n(1) As suggested, we trained a SFT model directly on the RL dataset, incorporating the VisionZip mechanism. As shown in *Appendix Table 5 (VisionZip‡)*, this SFT model does **not outperform** the training-free version of VisionZip. We attribute this to the **limited diversity and coverage** of the RL data used for fine-tuning, which is much narrower than the large-scale, high-quality supervised data used by the official Qwen team (Lines 248–253).\n\n(2) In *Appendix C.1*, we further justify our choice of RL over SFT. Empirically, the RL-trained policy demonstrates better resolution control: it learns **when to answer directly** and **when to request higher-resolution input**. In contrast, the SFT model triggers image resizing far more frequently across all benchmarks, indicating weaker policy learning (Lines 267–269).\n\nWe will revise the main text to better surface these findings and clarify the rationale for using RL in our method.\n\n\n> ### [Q4.3] Weakness 2.3: Comparison with keyword-based model / token selection\n\nThank you for the constructive suggestion. We address this concern from two perspectives:\n\n* In real-world deployment, it is **infeasible to accurately classify each input sample** based solely on keywords. Whether a high-resolution image is necessary depends not only on the question type but also on the **specific visual content**. For instance, in the last example of *Appendix Section D (Qualitative Results)*, the question “Are there any fire hydrants here?” contains no OCR- or ChartQA-related keywords, yet resolving it still requires high-resolution evidence.\n\n* In contrast, our VisionThink model is **end-to-end and data-driven**. It automatically learns a resolution policy conditioned on both the image and the question, allowing it to adaptively reduce image token usage without compromising performance.\n\n\nWe will emphasize this comparison in the revised manuscript to better highlight the advantages of our approach over the keyword-based strategies.\n\n\n\n> ### [Q5.1] Weakness 3.1: More QA-pairs examples\n\nThank you for the suggestion. We have included several qualitative examples in *Appendix Section D* to illustrate our model’s behavior. We will further enrich this section with additional case studies in the camera-ready version to provide a more comprehensive view of the model’s behaviors.\n\n\n> ### [Q5.2] Weakness 3.2: Choice of special token for upscaling\n\nThank you for the insightful question. As discussed in *Appendix Section C.3 (Different Prompt Impact)*, we conducted experiments comparing various prompt designs and special tokens for triggering image upscaling. The results show that following the **Qwen official cookbook-style prompt and special token** yields the best performance. We will move this analysis into the main paper in the camera-ready version to make the rationale and empirical evidence clearer to readers.\n\n---\n## Other Weaknesses\n\n\n> ### [Q6] Writing Suggestions\n\nWe sincerely appreciate you for your responsible and detailed review, as well as the constructive writing suggestions. We have carefully revised the paper according to your feedback. Key improvements include:\n\n1. Replaced the term *\"compress tokens\"* with *\"reduction tokens\"* for better clarity.\n2. Refined the motivation section to emphasize the memory and latency constraints caused by long visual token sequences, especially on edge devices.\n3. Updated the caption of *Figure 3* to:\n   > Figure 3: Impact of the Penalty Ratio. Applying a penalty to all image-resizing requests or removing the penalty entirely will both lead to model collapse.\n4. Moved more details of the Accuracy Reward mechanism to Line 149 for better visibility.\n5. Added proper citations for all baselines, e.g.:\n\n   | Method       | Benchmarks |\n   | ------- | ---------- |\n   | SparseVLM [72] (ICML 2025) | —          |\n   | FastV [7] (ECCV 2024)      | —          |\n   | VisionZip [58] (CVPR 2025) | —          |\n6. Clarified throughout the paper that **inference time is reported in seconds**, not milliseconds or FLOPs.\n\nThese revisions will also be reflected in the camera-ready version. Thank you again for your helpful input.\n\n---\n**Thank you again for helping us improve the paper and hope our response can resolve your concerns! Please let us know if you have any further questions. We will be actively available until the end of rebuttal period. If you feel your concerns are addressed, please consider reevaluating our work. Looking forward to hearing from you :-) !**"}, {"review_id": "ynLbEuTT9A", "reviewer": "Reviewer_Ubhv", "summary": "This paper introduces VisionThink, a novel paradigm to improve both efficiency and performance by dynamically adjusting image resolution. The core idea is to initially process a downsampled image and, if necessary, autonomously request a higher-resolution image. To achieve this, VisionThink combines reinforcement learning and LLM-as-Judge strategy for effective training on general VQAtasks. A reward function with a penalty mechanism prevents the model from always requesting high-resolution images.", "questions": "- Figure 5 shows that OCRBench has over one-third of its questions directly answered, yet Figure 4 demonstrates very little acceleration effect for OCRBench. What's the reason behind this discrepancy?\n- The \"LLM as eval combined with RL\" strategy frequently exhibits completely opposite performance between full-image resolution and mixed-image resolution. Could you elaborate on this issue? \n- Although the paper ablates the impact of different penalties on the Image Resize Call Ratio, it doesn't show specific performance changes. Is the model sensitive to penalty settings? How do different penalties affect the results on various benchmarks?\n\n**I would be happy to raise my score if these weaknesses and questions can be addressed.**", "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- Unlike previous efficient VLM methods that rely on fixed pruning ratios or thresholds, VisionThink autonomously decides whether to compress tokens on a case-by-case basis.\n- The extensive experiments show that VisionThink achieves substantial savings in inference time on most benchmarks.\n\n**Weaknesses**\n- Similar to how RL combined with additional image processing can be used for \"zoom in\" operations to introduce fewer visual tokens, it's necessary to explain the advantages of upsampling compared to zooming in.\n- The impact of benchmark characteristics on the method needs further specific analysis. For example, how would the acceleration effect and performance change if the benchmark inputs tend to be high-resolution or low-resolution images. These can help further understanding of the method.\n- For fine-grained understanding where the original image resolution is already quite large, the acceleration effect may be weaker because the additional inference overhead from an extra inference step is relatively high.\n- Although the acceleration effect compared to Qwen-RL has been demonstrated, further comparison of the inference efficiency with other efficient VLMs is also needed.", "was_revised": true, "final_justification": "Thank you for the authors' dedicated response. I am pleased to see work that introduces Reinforcement Learning (RL) to token acceleration. However, this implies that RL needs to be re-applied for different models. Additionally, considerations about the most appropriate scale for resizing and whether the process can be extended from two to multiple times are necessary. The issues that arise with the method are ambiguous, I will maintain my original score of Borderline accept.", "rebuttal": "Dear Reviewer Ubhv,\n\nMany thanks for your constructive and insightful feedback! We are glad that you found our VisionThink propose a novel paradigm. We have revised the paper following your suggestions, and will address both your questions and weaknesses below.\n\n---\n## Questions\n\n> ### [Q1] Discrepancy in Time Acceleration on OCRBench\nThank you for the insightful question. We clarify this from two aspects:\n\n1. On OCRBench, the upper bound for efficiency is `Qwen-RL (1/4)`, which always uses a 1/4-resolution image, resulting in roughly **50% time savings** compared to full-resolution input. In contrast, our method uses 1/4 resolution in **~38%** of the cases and requests high-resolution input for the remaining **~62%**. Achieving a **\\~20% overall time reduction** under this mixed policy is therefore consistent and reasonable.\n\n2. Additionally, we emphasize that our reported time is **real-world GPU runtime**, not theoretical FLOPs or token counts. As a result, this may introduce variance from multiple system-level factors, but it more accurately reflects practical deployment scenarios.\n\nAll evaluation code and measurement procedures will be released to ensure reproducibility. We welcome continued community oversight.\n\n\n> ### [Q2] Opposite performance between full-image resolution and mixed-image resolution.\n\nThank you for the insightful question. In a few benchmarks, we observe that the mixed-resolution outperforms the full-resolution setting. We believe this result arises from two key factors:\n\n1. For certain samples requiring resize, the model processes the same image twice at different resolutions, effectively gaining a dual-view perspective. This may serve as a form of **implicit data augmentation**, yielding marginal performance gains.\n\n2. More importantly, the **diverse sampling during RL training** enables the model to explore a broader policy space. This exploration may occasionally yield better policies, especially on specific benchmarks.\n\nWe will clarify this observation in the final version. Thanks again for your point it.\n\n> ### [Q3.1] Model sensitivity to the penalty settings\n\nThanks for your professional questions, and we have discussed this problem in the *Appendix Sec C.4 Ablation Study on Penalty Control Threshold*. The results show that, within an appropriate range, the model’s behavior is not highly sensitive to the\nexact threshold value.\n\nThanks again for your point it out, we will consider move this part into the main paper to make it clearly.\n\n\n> ### [Q3.2] How do different penalties affect the results on various benchmarks?\n\nIn *Appendix Figure 3*, we show how the penalty influences model behavior. Here, we further provide quantitative results across various benchmarks.\n\nAs shown in the table below, we report the performance and inference time of models trained with different penalty ratios. Overall, the trends are consistent with *Appendix Figure 3*: as the penalty ratio increases, the model becomes more inclined to resize the input image, leading to higher inference time.\n\nIn **general VQA scenarios**, model performance does not significantly improve with increased inference time. However, for **fine-grained understanding scenarios** (e.g., *ChartQA*, *OCRBench*), increasing the penalty ratio encourages more image resizing, which results in both longer inference time and improved performance.\n\n| Penalty   | ChartQA Performance | ChartQA Time | OCRBench Performance | OCRBench Time | MathVista Performance | MathVista Time | MME Performance | MME Time | MMMU Performance | MMMU Time | RealWorldQA Performance | RealWorldQA Time |\n|--------------|---------|--------|-----|--------|-----------|--------|-----|--------|------|-------|------|---------|\n| ratio=0      | 63.0    | 353.2s  | 70.3| 146.0s  | 65.6      | 1097.1s |2272 | 405.5s  |50.1 | 308.5s |64.8 | 112.3s   |\n| ratio=0.15   | 74.1    | 629.5s  | 75.8| 184.7s  | 65.7      | 1306.5s |2347 | 465.2s  |49.4 | 504.7s |66.8  | 198.4s   |\n| ratio=0.2    | 79.8    | 746.1s  | 80.8| 211.8s  | 67.5      | 1745.5s |2400 | 653.0s  |51.2  | 608.4s |67.1  | 235.6s   |\n| ratio=0.25   | 80.2    | 725.0s  | 83.5| 425.5s  | 67.9      | 1334.6s |2278 | 938.3s  |50.7 | 570.0s |67.5 | 361.9s   |\n| ratio=0.3    | 81.0    | 1033.4s | 84.8| 435.6s  | 66.6      | 1666.0s |2235 | 1932.7s |49.0  | 866.9s |69.3 | 492.8s   |\n|w / o Penalty | 82.1    | 1093.6s | 85.3| 437.1s  | 67.5      | 1721.9s |2354 | 1843.8s |50.7 | 820.5s |68.0 | 436.1s   |\n\nThank you for your constructive question. We will include these results in the paper, as we believe they significantly enhance the completeness and depth of our work.\n\n\n\n---\n## Weaknesses\n\n> ### [W1] Advantage comparison to zoom-in strategy\nThank you for the professional question. We see the advantages of our approach over zoom-in strategies from two key perspectives:\n\n1. Zoom-in methods require **strong localization capabilities**, which current models like Qwen2.5VL-7B do not fully possess. Inaccurate localization can degrade performance.\n\n2. Zoom-in may require multiple iterative steps due to imprecise region proposals, potentially leading to **higher total token consumption** than our strategy.\n\nHowever, we also agree this is a very promising direction and have already noted it as part of our future work (Line 285). We will incorporate this discussion into the camera-ready version to better contextualize the comparison.\n\n> ### [W2] Impact of benchmark characteristics on method behavior\n\n**Settings.** We selected the OCR-related benchmark *ChartQA*, along with two benchmarks representing more general VQA scenarios: *MME* and *RealWorldQA*. For each, we computed the ratio of samples that triggered image resizing under different input token ranges.\n\n**Results.**\nAs shown in the table below, there is no clear correlation between image size (token range) and the likelihood of image resizing. Instead, the **task type itself** has a stronger influence on the model’s behavior. For example, *ChartQA* consistently exhibits higher image-resize ratios across all ranges, whereas *RealWorldQA* remains low. This suggests that **task characteristics**, rather than resolution alone, play a dominant role in guiding VisionThink’s resizing decisions.\n\nThank you again for your suggestion. We will include this analysis in the camera-ready version of the paper to make our discussion more complete.\n\n| Token Range | MME (%) | RealWorldQA (%) | ChartQA (%) |\n| ----------- | ------- | --------------- | ----------- |\n| 0–600       | 32.8    | 8.0             | 84.0        |\n| 600–900     | 26.0    | 5.0             | 63.0        |\n| 900–1 500   | 39.1    | 19.7            | 51.1        |\n| >1 500      | 21.3    | 32.4            | 61.5        |\n\n\n> ### [W3] Efficiency on high-resolution images\n\nThanks for pointing this out. Following your suggestion, we conducted further analysis.\nAs shown in the table below, we focus on DocVQA, the benchmark with the highest image resolution. Our results show that VisionThink achieves **significant acceleration** while maintaining **comparable accuracy.**\n\nWe believe the reason is that, for high-resolution images, even after resizing (e.g., halving both height and width), the image remains **sufficiently clear** to solve most questions — even on fine-grained tasks like DocVQA. \n\nWe will clarify this point in the camera-ready version. Thank you again for the helpful suggestion.\n\n| Task | mean resolution | metric |Qwen-RL | Qwen-RL (1/4) | VisionThink |\n| ----------- | ------- | ------ | --------- | ----------- | ----------- |\n| DocVQA   | 2099 $\\times$ 1783 (H$\\times$W) | Accuracy    | 95.1            | 94.3        | 94.4 |\n|       |   | Time | 3076s            | 1327s        | 1355s |\n\n\n> ### [W4] Further comparison with additional efficient VLMs\n\nThank you for the valuable suggestion. In *Appendix B.6 (Comparison with Previous Efficient VLMs)*, we have included additional comparisons with recent state-of-the-art efficient VLMs.\nWe appreciate your reminder and will move this section into the main paper in the camera-ready version to make the comparison more prominent and accessible to readers.\n\n---\n**Thank you again for helping us improve the paper and hope our response can resolve your concerns! Please let us know if you have any further questions. We will be actively available until the end of rebuttal period. If you feel your concerns are addressed, please consider reevaluating our work. Looking forward to hearing from you :-) !**"}]

Author rebuttal:

Meta-review: This paper introduces a vision-language model that adaptively adjusts the number of visual tokens it processes. The model initially works with a low-resolution image and dynamically utilizes higher-resolution tokens only when it determines they are necessary. This on-demand approach offers a practical solution to the trade-off between computational efficiency and accuracy.

The review process for this paper was highly valuable, marked by thorough discussion and constructive engagement between the authors and all reviewers. This collaborative effort has strengthened the paper and led to a unanimous recommendation for acceptance. The authors' commitment to open-sourcing their data, code, and models is a commendable contribution that will undoubtedly benefit future research in this area. We look forward to seeing the final version of the paper, which is expected to incorporate the valuable points raised during the discussion phase.
Decision: Accept (poster)