Title: VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Vision Language Models

Reinforcement Learning
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n- 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.**\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\nPlease 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nYes, the authors have discussed limitations in the manuscript.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nSee weaknesses above.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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?\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}