ERGO: Efficient High-Resolution Visual Understanding for Vision-Language Models
Authors: Jewon Lee
Wooksu Shin
Seungmin Yang
Ki-Ung Song
DongUk Lim
Jaeyeon Kim
Tae-Ho Kim
Bo-Kyeong Kim
Venue: ICLR 2026 Poster (2026)
Keywords: Efficient large vision-language model
Reinforcement learning
Multimodal reasoning
Reasoning for efficiency
Primary area: generative models
TL;DR: ERGO is a large vision–language model trained with reinforcement learning on efficiency objectives, focusing on task-relevant regions to enhance accuracy and achieve up to a 3× speedup in inference.

Abstract: Efficient processing of high-resolution images is crucial for real-world vision–language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial computational overhead due to the large number of vision tokens. With the advent of "thinking with images" models, reasoning now extends beyond text to the visual domain. This capability motivates our two-stage "coarse-to-fine" reasoning pipeline: first, a downsampled image is analyzed to identify task-relevant regions; then, only these regions are cropped at full resolution and processed in a subsequent reasoning stage. This approach reduces computational cost while preserving fine-grained visual details where necessary. A major challenge lies in inferring which regions are truly relevant to a given query. Recent related methods often fail in the first stage after input-image downsampling, due to perception-driven reasoning, where clear visual information is required for effective reasoning. To address this issue, we propose ERGO (Efficient Reasoning & Guided Observation) that performs reasoning-driven perception—leveraging multimodal context to determine where to focus. Our model can account for perceptual uncertainty, expanding the cropped region to cover visually ambiguous areas for answering questions. To this end, we develop simple yet effective reward components in a reinforcement learning framework for coarse-to-fine perception. Across multiple datasets, our approach delivers higher accuracy than the original model and competitive methods, with greater efficiency. For instance, ERGO surpasses Qwen2.5-VL-7B on the V* benchmark by 4.7 points while using only 23% of the vision tokens, achieving a 3× inference speedup.  The code is available at https://github.com/nota-github/ERGO.

Published as a conference paper at ICLR 2026
ERGO: EFFICIENT HIGH-RESOLUTION VISUAL UNDERSTANDING FOR VISION-LANGUAGE MODELS
Jewon Lee'*
Wooksu Shin
Seungmin Yang'
Ki-Ung Song'
DongUk Lim
Jaeyeon Kim
Tae-Ho Kim
Bo-Kyeong Kim
Nota Inc.
(jewon. lee,
wooksu. soin,
thkim, bokyeong kim kim) @nota. ai
ABSTRACT
Efficient processing of high-resolution images is crucial for real-world vi- sion-language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial computational overhead due to the large number of vi- sion tokens. With the advent of "thinking with images" models, reasoning now extends beyond text to the visual domain. This capability motivates our two-stage "coarse-to-fine" reasoning pipeline: first, downsampled image is analyzed identify task-relevant regions; then, only these regions are cropped at full res- olution and processed in subsequent reasoning stage. This approach reduces computational cost while preserving fine-grained visual details where necessary. A major challenge lies in inferring which regions are truly relevant a given query. Recent related methods often fail in the first stage after input-image down- sampling, due to perception-drive -driven reasoning, where clear visual information is required for effective reasoning. To address this issue, we propose ERGO (Effi- cient Reasoning & Guided Observation) that performs reasoning-dres driven percep- lion-leveraging multimodal context determine where to focus. Our model can account for perceptual uncertainty, expanding the cropped region to cover visu- ally ambiguous areas for answering questions. To this end, we develop simple yet effective reward components in reinforcement learning framework for coarse-to- fine perception. Across multiple datasets, approach delivers higher accuracy than the original model and competitive methods, with greater efficiency. For instance, ERGO surpasses Qwen2.5-VL on the V benchmark by 4.7 points while using only 23% of the vision tokens, achieving a 3x inference speedup. The code is available at https::/
com nota github/bhhtg /RROO
INTRODUCTION
High-resolution image processing is crucial to achieve strong performance in real-world applica- tions with large vision-language models (LVLMs) (Liu et al. [202aa] Wang et al. [202aa] Vasu et al., 2025 Recent reinforcement learning (RL)-based post-training approaches (Wang et al. 2025a Zheng et al., 2025b) have explored the idea of "thinking with images" (OpenAI] 2025). enabling LVLMs to reason not only through text, but also within the visual modality itself. By reason- ing over cropped image features with bounding bounding-box box coordinates, these models can attend to local high-fidelity objects and capture fine-grained details, leading to significant improvements in high- resolution benchmarks.
Despite these advances, processing high-resolution input remains a major challenge. LVL.Ms must handle massive number of vision tokens, resulting in prohibitive computational costs. A straight- forward solution (Zhou et al., 2025; Yang et al. 2025). is to reduce the input resolution, which results in fewer vision tokens but inevitably discards fine-grained details critical reasoning. The two-stage "coarse-to-fine pipeline embodies this principle: it first queries the model with a coarse- grained image for initial reasoning over task-relevant regions; and then selectively localizes and re- encodes sub-images at higher resolution with finer granularity for subsequent reasoning. Crucially. discovering relevant regions from downsampled image input is fundamental to overall performance, as it guides the model to focus its capacity on informative areas.
Fig. illustrates this challenge and our solution. In Fig, aa), DeepEyes (Zheng 2025b)
performs well when the target object remains clearly visible (i.e., correctly identilying a straw in the
"These authors contributed equally and are listed alphabetically.
Cooresponning authors.

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Published as a conference paper at ICLR 2026
(t) Vision Token Efficiency
(b) Vision Token Efficiencc
(c) Vision Token Efficiency
Acturate Reasoning
Accurate Reaseming
Accurate Reasoning
dicemnilee
strew indaceenible dromsamping
What is the oolor of the strw??
What is the color of the sirew??
The signs are colorful, and there are straws visitle the image. The straws appear to
The strass are Ikeey located near the
coffee region
table
whee
What is the color of the straw?
The model fallz to Find the straw and instead reasonts about [abse objecd.
ce,
uses coarse cues
Looking the focus the coffee
and strees C
Suroesd daly located the siraw
O
Crop at righ-resttutonn
hiph-resolution
The zoom-in contirms that
The visible the enlarged
straws are indead white... The color of the straw white.
image near the coffeee on the table.
the image shows Powerful ful, bu: Loo much computation cost from maestve image takens,
black.
The straw appears to be black...
Falled to solve the Lask with
Correctly ident/fied straw's location and porer Me ptrertion reeAgring.
downsampled Image.
Comparison with prior work on high-resolution visual reasoning. The yellow box marks the target object, which becomes indiscernible after input-image downsampling. (a) Deep- Eyes (Zheng et al.) 2025b) succeeds when the object remains discernible, but at the cost of a large number of vision tokens. (b) DeepEyes (Zheng al. 2025b) fails when the object is indiscernible at low resolution, where fewer vision tokens are available. (c) Our ERGO performs reasoning-driven perception, correctly answering the question even low-resolution images.
Figure I:
high-resolution image), but it requires processing a large number of vision tokens. Relevant models (Wang et al., [2025a] (Su al.) 2025a Zheng et al.) 2025b) are typically designed in this perception- driven reasoning paradigm, where the model first localizes a tightly bounded target and then reasons over it. As a result, their training tends overlook downsampled visual inputs. While effective at full resolution, this paradigm becomes bottleneck in efficiency-oriennee scenarios.
After umput-image downsampling for smaller number of vision tokens (see Fig. [b)), the straw becomes indistinguishabl, causing DeepEyes (Zheng et al. (2025b) to miss it and incorrectly focus on more discernible objects. contrast, under such pixel-constrained conditions, our approach (Fig. [c)) highlights that reasoning-driver perception (i.e., including contextually inferable regions such as straws near coffee cups on tables) is far more beneficial, since selecting the correct region enables recovery of the original resolution in that area.
We introduce ERGO (Efficient Reasoning & Guided Observation), whose training objective is ex- plicitly aligned with proiesssoo efficiency in reinforcement learning (RL) framework. It rewards the inclusion of all task-relevant regions, while implicitly incentivizing the incorporation of auxiliary context. This design enables the model to handle ambiguity without being restricted to precise localization, learning that exact identification of individual objects is not always optimal and that reasoning with contextual knowledge is often more beneficial. By aligning visual exploration with efficiency objectives, our approach enables LVLMs to achieve improved efficiency without sacrificing fine-grained reasoning ability. Our key contributions can be summarized as follows.
Efficient coarse-to-fine pipeline. We introduce a two-stage reasoning pipeline that first pro- cesses low-resolution inputs to identify task-relevant regions and then re-encodes them at higher resolution. The pipeline reduces computational cost while preserving essential information.
Reward for reasoning -iroonnn--drveen perception. With our proposed reward, the policy model learns that relying solely on accurate object localization is not always optimal and that contextual knowl- edge can often be more beneficial. To our knowledge, we are the first to demonstrate the signifi- cance of this insight for reghhresolution visual processing in LVLMs.
State-of-the-art performance with fewer vision tokens. ERGO surpasses competitive methods (Huang et al. 2025b: Yang et al. 2025 Zheng et al. 2025b Wang al. 2025a Su et al., [202aa] Lai et al. (0225) ) in accuracy on multiple high-resolution benchmarks, while reducing vision token
counts and delivering practical speedups.
2

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Published as a conference paper at ICLR 2026
Trainable
Conventional rewords
Frozen
Tormas
Policy Model
Crop
Policy Model
I Torig q
Oregion
regtion
Oace
Tace
Tbox
I regions q
Reward
Model
Fregion
Xa
TCCB
R
Proposed rewords
I loig is not used for the Tregion-
Figure 2: Overview of RI--based training pipeline. The blue background highlights the com- ponents of the proposed TCE reward. The purple background highlights the conventional rewards adopted by most reasoning LVLMs.
2
MOTIVATION
Pixel const.
Tusk region
V*
We examine whether appending a critical high-resolution sub-image to low-resolution image input can enhance model performance. For the experiments, we used Qwen2.5-VL, (Bai et al. (2025) with pixel constraints to resize input images. Specifically, a setting of Nx28x28 in its image processor caps the maximum number of vi- sion tokens at N. We varied the input resolution by con- trolling N and included the ground-truth (GT) original- resolution sub-image as an auxiliary input. Tab. I shows that using the GT full-fidelity sub-image does not degrade performance, even when the model has not been explic- itly trained under such conditions. This finding indicates that high-resolution access to task-relevant regions is sufficient, whereas redundant tokens merely reduce efficiency.
16384x28x228
X
77.0
1280x28x28
X 64.9
640022xx28
X
56.5
640028x22
77.0
Effectiveness of high- resolution task-relevant cues. "Task- relevant region" denotes whether the annotated GT sub-image at original resolution is appended to the input. Evaluation was conducted using Qwen2.5-VL-7B on the V* benchmark.
Table
Now that we have shown the effectiveness of tesk-relevant regions, the next question is whether ex- isting models can autonomously identify such regions. A straightforward strategy might integrate powerful "thinking with images" model (Zheng et al. 2025b Su et al. 2025a Huang et al. 2025b) Wang et al. 2025a) into the coarse-to- pipeline, predicting grounded coordinates and cropping the corresponding high-resolution sub-images. However, results show that existing RL-trained reasoning models struggle to perform this task under low-resolution inputs (see Tab, 2 This high- lights the need for approaches that can robustly identify informative regions even when coarse visual cues are the only available signals, rather than relying solely on clearly discernible objects.
PROPOSED METHOD
Our objective to develop remarkable beasoning-driven perception models that can reason over where to focus. Fig. presents our RL-based training pipeline, whose forward process is as follows:
Given pair of original image lorig and text query q, the policy model my produces output Oregion (- Longg 9). which includes candidate bounding box coordinates (indicating the region relevant to the query) and thinking trace.
Next, the image Tregion corresponding to the bounding box cropped from the original image lorig feed into the reward model: Legiin + crop(Jorgg Oregion).
Then, the policy тo generates an answer Oace ~ Te [rreion q), [arigg Oregion)) in multi-turn conditioned setting, based on both the past interaction (i.e., original image Long and predicted bounding box Oreiion) and the current query pair (i.e., cropped region Leggio and text query g).
The strength of ERGO lies in well-designed reward components for coarse-to tooee-tt-finee fine vision- ssiion-grouned reasoning, detailed as follows.

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Published as a conference paper at ICLR 2026
3.1 REWARD DESIGN
3.1.1 PROPOSED REWARD
Region-verificatoon reward. In many thinking- Seetiinn-wtt-iiinn with-images studies (Huang et al. 2025b; Su et al. 2025a Zheng al. 2025b), a reward model R takes the original image together with the cropped region and query, producing its output ORM
Lorig /region q) to guide the policy model. How- ever, we argue that feeding the original image long into the reward model sub-optimal: the model may rely the original image instead of the cropped region, introducing unnecessary hints to the query and thereby weakening the objective of self-contained cropping (i.c., ensuring the cropped re- gion alone provides sufficient cues). This issue is particularly problematic for coursc-to-fine visual grounded reasoning, which we adopt for efficiency, because low-resolution input images contain little evidence (as target objects are often indiscernible), making self-contained crops essential for question answering.
To address this issue, we propose the region-verification reward Fregion. where task performance is evaluated using only the cropped region and the query, without access to the original image. We reframe the complex task of locating the optimal region into the simpler task of answering the question with a single cropped image. If the reward model's prediction matches the GT answer OGT, the policy model receives a reward:
ORM ~ R- regionn 4):
Tregiom = VF [match(ORMM (CT))
This design encourages the policy model to identify informative, task-relevant regions that preserve sufficient information for accurate reasoning. without the need for additional annotations. In prac- tice, we use a frozen reward model, Qwen2.5-VL-722 --ee2.5-VV-72B-Insttree (Bai et al. (025).
Box adjustment reward. Although the region reward effectively encourages task-guided cropping, a key challenge emerges during early training: the policy model may exploit trivial strategy by consistently selecting the entire image. While this would be a reasonable shortcut for maximum region reward, since the whole image is necessarily self-contained for the task, it limits efficient inference due to excessive token costs from processing the full-resolution image.
To mitigate this issue, we introduce complementary reward signal that regularizes the size of the selected region. Specifically, the box adjustment reward Tbox is computed with a step function that penalizes overly large crops based on the area ratio of the selected region to the original image; effectively prevents the model from consistently grounding the entire image:
AAraa Area(Lorig) S
Tbox
*
(2)
Determining an ideal value of y is crucial for our ap- proach: low enough to prevent degenerate solutions (e.g., selecting the full image as the crop) during training, yet high enough to allow flexibility in re- gion selection. To this end, we examined the training datasets with answer-aligned bounding box annotations (c.g.. TGR (Wang et al.) 2025a). VisCoT (Shao et al.) 2024a), V (Wu & Xie 2023). VGR (Wang et al. ).25b)). Fig. Fig. shows that most GT regions relevant to question answering occupy less than 60% of the full image. Based on this analysis, we set = 0.6 for efficient and effective bounding box adjustment.
TraNVR
VisCol
Communtivv Probakiity
Cannkaii winnaaaiiee.. Ponbukiity
123
Preguarry
split of popular LVLM reasoning-relaan
64
19th
24
Geound Regicn foes Batto
Ground Reglor Area Ratie VGR
Probobbiiity
0.58
Nator Protahiil
Ereeueeryy
30
Gewond Truth Region Ares Robio
Ground Truth Ruginn Hahlo
12
Task-driven contextual exploration (TCE) re- ward. Based on the collaborative nature of the re- gion reward and box adjustment reward, we combine them to form our main reward PrcE:
Figure 3: Analysis of query-relevant GT regions in training data. Most GT regions span less than 60% of the full image area.
FTCE -Tregonn + 3 Thox-
(3)
4

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Published as a conference paper at ICLR 2026
Here, and 3 are weighting coefficients, set to 1 and 3 0.5. This enables the policy model to learn robust and efficient region selection strategies for vision-grome iriiongroouned reasoning.
3.1.2 CONVENTIONAL REWARD
Accuracy reward. The TCE reward is effective to guiding the policy model select task-relevant regions. However, it only indirectly promotes correct question-answering, creating a potential mis- match between the training objective and the final evaluation. To bridge this gap, we use an accuracy reward (DeepSeek-Al et al. 2025). which is assigned when the policy model's output Once matches the GT answer: Face = IF Fmmatc((Oucs Our)]. This component complements the TCE reward by directly optimizing for question-answernt
accuracy.
Format reward. This reward enforces the adhesion to a predefined output structure us- ing special tags (DeepSeck-AI al. 2025). A reward is given if the reasoning is correctly tags, the final answer within <answer></answer> tags, and <zoom></zcom tag is included when region selection is performed: Tiomaa H Oregion: Oace follow expected format J. This mechanism encourages the model to maintain well- formed outputs that can be reliably parsed and evaluated throughout training and inference.
enclosed within <thirk></thinks
3.1.3 FINAL REWARD FORMULATION
The overall reward function is defined as linear combination of three components (i.e., the TCE reward, the accuracy reward, and the format reward):
R = PTCE + + Plormal
(4)
3.2 LEARNING ALGORITHM
We adopt Grouped Reward Policy Optimization (GRPO) (Shao et al. 2024b) as our RL framework, leveraging its sample-efficient optimization in grouped feedback settings (see the pseudo-code in Sect. for details). Through this effective RI training, ERGO acquires reasoning-driven perception capabilities when presented with resouuoouu,on, target-indiscemible inputs.
EXPERIMENTAL SETUP
the policy model and (Bai et al. 2025 as the frozen reward model. Our training data con- 2024 and the V* training set (Wu & Xiel 2023). following Zheng et al. (2025b). Training was conducted with global batch size of 128, using 8 rollouts per example 4 H100 GPUs. See Sect. for full details.
Training setup. We
Qwen2.5 Owen2.5-VL-7B InItrrcct (Bai al. 2025)
Qwen2.5-VL
sists of subset of ArxivQA (Li al.
Baselines. We compare our approach against two categories of RL-based post-training methods:
o Efficiency-orrinteed models share our objective efficient high-resolution vasion-language un- derstanding. MGPO (Huang et al. 2025b) directly leveraged the multi-turn pipeline but with single reasoning stage. VisionThink (Yang et al. (025) does not select sub-region of the orig- inal image; instead, it employs mechanism whereby the model, given downsampled image, determines whether the full high-resolution image should processed for the task.
models are considered due to their strong grounding capabilities, which could still benefit the coarse-to-tine pipeline. DeepEyes (Zheng al. 2025b). PixelReasoner (Su) et al. 2025a) and MiniO3 (Lai et al. 2025) are not trained for efficiency, but can be adapted to coarse-tor to-eitt--ieeeeee scenarios. Though TreeVGR VGR (Wang al., 2025a) is inherently incompatible with coarse-to-fine settings, as it performs text-only reasoning over bounding- coordinates rather than visual re-encoding, we include it as a baseline for strong grounding performance.
Non-efficiency-orient
Benchmarks. We utilize high-resolution visual question answering (VQA) benchmarks including V* (Wu Xie 2023), HR-Bench (Wang et al., all,|220 2024b), MME-RW MEE (Zhang et al. (022).. TreeBench Waag et al. 2025a) and VisualProbe (Lai et al. 2025), our objective efficient high-resolution
5

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Published as a conference paper at ICLR 2026
Pisel Const.
Model
V Bench
HR Bench
HR Bench"
MME-RW
TreeBench
VisuaPProbee
Average
16384x28228
Be.n22s-LL--B-lum
46.7
40.0
29.2
52.4
Qwen2.5 1eo.2.5-VL-7BBam
64,9
65.6
$6.5
42.6
40.0
15.2
44,8
Non-dikiency-rrime Training
PandRReamerr (Seetal
N075a
66.9
61.3
49.8
40.4
31.8
DeepPyes Zhehn o dI M0AA
78.5
66.0
60.0
48.9
40.2
39.0
52.5
1280x28x28
TreeVER
66.4
60.4
48.2
MiniO3
-iiieeey--rreeeee Post Training Metheds
MGPO (Hune al 20756
77.5
69.8
61.1
44.4
39.3
33.4
52.6
Viskon Yang
41.0
ERGO
83.8
73.0
69.9
52.6
41.7
42.7
58.4
Qwen2.5 VLL---l-
49.9
40.3
40.2
10.9
Non-dleiency yrrieeeedd Poor Training Methods
PizelReasme (n
N075
67.2
66.5
59.9
47.7
42.3
23.9
48.9
DeepEyes
38.5
610x28x28
TreeVGK Wang d
40258
67.0
62.4
54.4
47.5
49.1
24.1
49.22
MiniO3
Carepin 0133
74.9
62,8
57.3
34.4
36.0
31.7
48.2
Ayfeeency-orttutee Fost Methods
MGPO' [Hnang et al 2005b
67.5
62.8
57.3
44.4
42.0
29.7
48.7
Vision/Tank
10A5
61.8
66.9
60.1
16.6
39.5
45.9
ERGO
49.6
43.2
Table 2: Performance comparison under efficiency scenarios with pixel constraints. ERGO outperforms the original model and post-training methods across all benchmarks. f denotes reproduction with their code using our data, while denotes inference with their original pipeline.
Pixel Conse.
Model
of vision tokens
V+
16384x28x28
Qwen2.5VLL-78
4,471
77.0
PixciReasoner
1,563
74.3
DeepEyes
TreeVGR
1,244
76.4
1280028688
MGPD
1,315
77.5
Vision Think
1,749
73.8
MiniO3
1,581
81.2
ERGO
1,632
83.8
Score
70
640228228
1,025
Table Comparison of vision token counts in coarse-to-fine reasoning.
70
DeepEves
TreeVOR
Phelleasmeer
MolonThink
Latency
MGPO
16384428225
Qwen25 VL-78
Qwe92.5
2000
3500
60X28x22
Desplyes
Number of Vision Tokens
Figure 4: Performance-efficiene trade-off on the v benchmark. The total number of vision tokens the sum of the tokens from the downsampled origi- nal image and those from the high-resolution cropped image.
640x28x28
MiniOS
3,87
640x28x28
Table 4: Latency comparison with models that leverage multiple tool calls on V* us- ing the vLLM engine. Latency represents the average duration to produce a final an- swer for each image-query pair.
image understanding. To assess potential trade-off's introduced by training, also consider con- ventional multimodal benchmarks: CV-Bench (Tong et al., 2024a) and MMVP (Tong al. 2024b) as vision-centric benchmarks: Hallusion-Bench (Guan et al. 2024). POPE (Li et al. 2023), and MMBench (Liu et al., 22024b) as general-purpose VQA tasks; and AI2D (Kembhavi et al. 2016) and ChartQA (Masry et al. 2022) for chart understanding.
5
RESULTS AND ANALYSIS
5.1 MAIN RESULTS
High-resolution reasoning with efficient visual processing. To measure performance under efficiency-consideeed scenarios, considered two different pixel constraints 640x28x22 and

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Published as a conference paper at ICLR 2026
1280x28x28 corresponding to vision token limits of 640 and 1280, respectively. Since our goal is efficiency, ERGO was trained to perform reasoning with a single tool call, similar to other efficiency- oriented methods. As tool calls naturally increase lateney, some methods that do not prioritize effi- ciency may make multiple tool calls. For a fair comparison, we constrained the maximum number of tool calls four during all evaluations. Tab. shows that, because the average resolution of input images exceeds these limits, many baseline models cannot accurately reason over images, leading to performance degradation.
contrast, ERGO consistently outperforms all baselines across bench- marks. In particular, compared to Swve2.55VL--77 only ERGO achieves a higher score for every benchmark we evaluated, even under the strictest 640x28x28 pixel constraint, Qualitative results are presented in Fig. and Sect. D
Fig. shows that ERGO lies in the Pareto-optimal region, achieving higher scores with fewer vision tokens. We evaluate ERGO with multiple pixel constraints (320, 640, x280X228X28, corresponding (579, 1026, 1632) total vision tokens per sample, whereas competing baselines are evaluated at {640, 1280, 2560, the 1280x28x28 constraint, ERGO achieves the highest score within the same constraint group. Remarkably, at 640x28x28, ERGO outperforms all baselines while using fewer vision tokens than others evaluated at 1280x28x28. These results demonstrate that our model achieves highly efficient utilization of the vision token, the pixel constraints can be flexibly regulated at test-time.
Tab. 3 also shows that with the coarse-to-fing pipeline under
Practical latency improvements. To demonstrate that our method not only reduces vision token usage, but also provides practical benefits in real-world deployment, conducted latency compar- ison with the original Qwen2.5-VL VL-7B model. The evaluation was performed using the production grade vLLM engine (Kwon et al. 2023) on single H100 GPU with batch size of 16, measur- ing the time to produce a final answer for an image-query pair. Tab, Tab. shows that models leveraging multiple tool calls trade off efficiency for performance, whereas our approach achieves faster latency while simultaneously surpassing them in accuracy on the V* benchmark. See Sect. C for additional aatency-performane trade-off analysis.
5.2 IN-DEPTH ANALYSIS
Leveraging contextual information for VQA. We show
that the superior performance of our model arises from
Target Coverage Score
80
abj. unmaslieed obj.. musked
80.4
its ability to identify task-relevant regions even when tar-
get objects become visually indiscernible (see Fig. [ To
60
23.1
57.9
37.9
quantify this ability, we analyze whether the predicted re- gion covers the GT target object, by defining Target Cov- erage Score.
40
27.2
20
19.5
Arca(by by) Arca(by)
Target Coverage Score
Bar
Σ E
mAX byE
Deepeyes MGPO
TreeVGR ERGO
foaction of GT box coveral matching prstiction
Figure 5: Evaluation of model robust- ness under target-object masking.
where Bpred is the set of predicted bounding boxes per sample and Byr is the set of GT bounding boxes per sam- ple. We evaluate scores separately for cases where the object is completely masked (bounding box overlaid with black) and where it remains discernible. Since masking removes explicit visual rep- resentations of the object, models can only succeed by leveraging contextual information such as surrounding visual context or textual cues. Fig. shows that ERGO achieves the most robust per- formance in the masked condition, consistent with its stronger ability to exploit such contextual signals.
Bras-free region prediction with the box adjustment constant. We employ fixed box adjustment constant (i.c., 7 is used in Tbox of Eq. P to facilitate efficient training; in principle, the model could be guided to predict the largest region permitted by the constant. However, Fig. shows that ERGO infers regions with flexible areas that reflect the underlying characteristics of the data: in MMVP (Tong al. 2024b), objects often occupy the full frame, whereas in MME-RWI (Zhag et al. 2024). objects are relatively small. This indicates that the box adjustment constant does not bias ERGO toward fixed-size predictions.
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Published as a conference paper at ICLR 2026
(a) MME RealWorld Lite
(b) MMVP
Benchmark
Qwen2.5-VL.
ERGO
CVBench-2D
74.1
76.0
Box odj, reward constont
CVBench-3D
73.0
80.3
Froqueney
150
Froquency
MMVP
77.0
77.7
Hallusion Bennch
47.1
52.3
100
POPE
86.4
87.4
MMBench
82.9
A12D
81.3
84.7
0.2
0.4
0G
0.4 04
ChariQA
86.1
85.8
Predicted Region Arca Ratio
Predicted Region Arca Ratio
Figure 6: Bias-free region prediction. ERGO adapts re- gion sizes properly for (a) the high-resop high-resolution MME-RWL and (b) the low-resolution MMVP, indicating that the box adjustment constant does not bias the region predictions.
Table 5: Results conventional vi- sion-language benchmarks. ERGO maintains improves the capabilities of the base Qwen2.5-VL-7B model.
No.
Method
Face
Fregion
Tbox
RW
Avg.
Qwen2.5-VL-7B
52.4
No.
[a,
Avg.
Tace only
53.5
(i)
(1.0, 1.0)
56,2
Tregion only
(ii)
(1.0, 2.0)
54.3
+box adj. reward
54.9
(iii)
(0.5, 0.25)
56.8
+reward weighting (RW)
55.3
(iv)
(1.0. 0.5)
58.4
ERGO
58.4
(b) TCE reward weight
(a) Reward design
Parameter size
Average
Reward model
Average
Function
Average
3B
55.6
GLM4.5V-1066
58.2
0.4
step step
57.8
0.8
$6.2
7B
Intern VL3-78B
57.2
slep
58.4
72B
58.4
Qwen2.5-VL-7221
58.4
0.6
linear anneal.
$1.0
(c) Parameter size
(d) Reward model
(e) Box adjustment reward
Table 6: Ablation analysis. Average performance measured over six benchmarks in Tab.
Results on conventional multimodal benchmarks. We evaluated ERGO on broad set multi- modal benchmarks, including general VQA, vision-centric VQA, and document understanding. As shown in Tab. 5 ERGO not only maintains the abilities of the base model but also achieves im- provements several benchmarks. We attribute these gains to the improved ability of the model to reason in semantically relevant regions.
5.3 ABLATION STUDIES
The TCE reward is more effective than the curacy reward. In Tab. Ma). D relies solely on the TCE reward, without generating task answers during training, whereas @ relies solely on the ac- curacy reward, without evaluating the quality of the cropped region. Remarkably, although the fi- nal performance is measured by answer accuracy,
Predieted Region Area Ratio
0.8
region
0.6
region
+ box adj. reward
D -hhihhh was never explicitly trained to answer task-still outperforms A. The results high- light the effectiveness of the TCE reward design,
0.2
because improving the quality of the selected re- gion with task-relevant evidence is critical to per- formance in the coarse-to-fime pipeline.
80
60
100
120
140
Training Steps
Figure 7: Impact of box adjustment reward predicted regions during training.
The box adjustment reward is critical for effec- tive training. In Fig. 7 removing the box regular- ization reward drives the model toward the trivial policy of cropping overly large regions. Evidenced
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by the superior performance of ( compared to L in Tab. Ba), the removal of the box adjustment reward not only causes inefficient inference but also impairs effective model training.
Regularizing the prioritization of the box adjustment reward is beneficial. For c in Tab, 6 we set a 1 and 3 = in Eq. 3 For D we reduced the weight of the box adjustment reward to 3 = 0.5 to prevent the policy from overly prioritizing this term coordination of the weights results in higher average scores, which confirms our intended effect. In Tab. (]b). we further investigate multiple configurations of and B. Configuration (ii), where the box-adjustment weight / is larger than a, shows the lowest performance. In contrast, config- uration (iii, which scales both a and by common multiplicative factor while preserving their relative ratio, outperforms configuration (i) even when the region verification signal is weakened. This further underscores the strength of our TCE reward formulation and validates the proposed weighting strategy.
more critical rewards. This
The accuracy reward is complementary to the TCE reward. In Tab Ga), while @ underperforms compared to C. combining the accuracy reward with the TCE reward can yield benefits during training. This complementary effect particularly valuable when the model successfully selects the task-relevant region but struggles to answer from the cropped sub-image. By leveraging both rewards, ERGO with R can address both the quality of the cropped image and the training-test mismatch, thereby enhancing overall performance.
Our reward design plays a critical role regard- less of size of the reward model and the model family. We evaluate the impact of reward model size by varying 2wen2.5-VLLInstree
Box adjustment reward
1.0
0.8
(Bai al.)
0.6
Linear annealing
2025) from 72B to 7B and 3B. As shown in Tab. cc), the performance does not collapse but remains robust, exhibiting only limited sensitiv- ity the model scale. Furthermore, Tab. d) shows that performance remains stable across dif- ferent reward model families. We attribute this robustness to the nature of our proxy task for as- sessing cropped image quality. Evaluating a re- ward model's 's response given image crop and query a fundamentally straightforward task, en- abling even lower-capacity reward models per-
Step function
0.4
0.2
0.0
0.2
0.4
1.0
Relative size of predicted region
Figure 8: Reward function for box adjust-
ment reward.
Helative predicted region
Linear annealing
form it reliably.
Step function (ERGO)
Data-driven selection of y is effective. Tab. o(e) supports our strategy for selecting the box adjustment constant 7 in Eq. 2 By setting 7 based on the data statistics, we effectively guide the model's training behavior. A large y would fail to penalize the trivial solution of cropping excessively large regions. Conversely, small would restrict the model's ability to explore and identify the most relevant visual areas. Our data-driven approach strikes a balance, encourag- ing focused exploration while maintaining train- ing stability.
0.1
0.0
200
250
Training steps
Figure 9: Impact of reward functionon pre- dicted regions during training.
Objective-aligned box adjustment reward degrades training performance. We experimented with linear annealing function to gradually emphasize smaller region selections, as shown in Fig. S which aligns with our efficiency goal reducing vision tokens. However, our empirical results indicate that annealing degrades performance: the policy becomes overly incentivized to shrink predicted regions, as illustrated in Fig. g because reducing region size is substantially easier than improving the region-verification reward. Consequently, Tab. shows that the model trained with linear annealing fails to reason over semantically meaningful areas.

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6
RELATED WORK
LVLMs reasoning on vision spaces. The remarkable reasoning capabilities of RL post-trained Large Language Models (LLMs) have significantly advanced problem-solving. Approaches such as GRPO (Shao et al. 2024b) demonstrate that grouped reward signals can effectively induce complex reasoning. Building on these advancements text-only LLMs, substantial efforts have been made to extend similar reasoning schemes to LVLMs. Early efforts (Shen et al., 2025 Huang et al. 2025a)
integrated vision inputs for reasoning, using GRPO-like techniques to improve LVLM reasoning with text-only exploration. More recently, the concept of "thinking with images" (Su et al. 2025b), exemplified by models such as OpenAl-o3 [OpenAT 2025). has gained traction, emphasizing visual- space reasoning. While reasoning LVLMs (Zheng et al., 2025b; Wang al., (025a) have been
widely studied to boost performance, their use for efficient inference remains under-explored. Our work addresses this by showing that ERGO with grounded region supervision achieve both higher efficiency and greater task-solving ability.
Efficient LVLMs with vision token pruning. The efficiency bottleneck lies in the rapid growth of vision token count as input image resolution increases. Vision token pruning (Chen et al. 2024 Wen et al. 2025b Lee et al. 2025) mitigates this by selectively removing tokens to reduce computation. However, these methods often rely on layer-specific inference schemes, making them unsuitable for production-grade engines (Kwon al. 2023 Zheng et al. 2024) that lack support for dynamic sequence lengths across layers. As noted by Wen et al. (2025a). such pruning often yields theoretical FLOPs reductions, which rarely translate into real inference-time speedups. Their focus is largely on compensating accuracy loss rather than achieving performance gains. In contrast, ERGO provides both performance gains and practical latency improvements within production-grade LLM engines.
Efficient LVLMs with RI. RL has been explored as method to improve the efficiency of LVLMs. straightforward approach, it comes with the trade-off of
While moderating image resolution reducing visual information. To address this, some RL-trained methods empower the model to man- age resolution itself. For instance, VisionThink (Yang et al./ 2025) trains models to request higher resolution when image is too ambiguous to answer question. However, this approach remains redundant, as it reprocesses the entire image at a higher resolution rather than focusing on task- relevant regions. In contrast, MGPO Huang et al. (2025b) trains models with downsampled images and high-resolution cropped regions, rewarding final answer accuracy. However, by neglecting the quality of the selected regions, MGPO fails to surpass methods without efficiency objective. By assessing predicted regions with afficiency-orientt objective, ERGO achieves the best efficiency in high-resolution visual understanding.
CONCLUSION
Our study reveals critical limitation of existing perception-driven reasoning models: their per- formance substantially degrades under low-resolution inputs in coarse-to-fine reasoning scenarios. These models rely heavily on clearly discernible visual anchors to localize objects; when such cues are lost due to downsampling, their ability to identify task-relevant regions deteriorates, causing emrors reasoning and question answering. This underscores the need for approaches that cap- ture coarse cues while selectively attending to semantically salient regions. Our ERGO conducts reasoning-d -diven perception, maintaining both efficiency and accuracy even when high-fidelity ob- ject information is lost, thereby overcoming the efficiency shortcomings of prior methods.
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ACKNOWLEDGMENTS
This research was supported by Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT (MSIT, Korea) Gwangju Metropolitan City.
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Appendix - ERGO: Efficient High-Resolution Visual Understanding For Vision-Language Models
A
TRAINING ALGORITHM
Algorithm 1: Policy updates with reward design
Input:
Policy model sTs. reward model R. train set [Longg, 4;, (. group size G. reward weights [3, box adjustment constant 7, stability constant
while training
foreach sample (Jorz 9, OCT) in the train set do Initialize empty lists: GroupRewards + D. GroupRollouts +
foreach rollout
1,..., G do Forig-
Oragion IT (-
Oregion is not valid for crop then
// e.g. impossible to parse out the bounding-boxx
missing value at the coordinates, etc.
PreE; flac, Pionmat +-
else
Tregion -- crrpp Tang; Oregion) )
Once To Ireion, g) [Torig, Oregion)
ORM R( Lragion q)
Tiegion
11 Mmaaccc (RM, OGT)]
Thox MM Areal ssaiie)
Areal Tong)
I <1
Tace 1f Kuaachh(om (Ouee DGT).
Treem
<- HK Oragions One follow expected format] format
/1 Task-driven Contextual Exploration (TCE). Reward
PICE Pregoon + + Pbox
< PTCE + Puce + Plorma
Append (Orepiony Osee) to GroupRollouts
Append R to GroupRewards
< EG GroupRewards(s)
on + g "Groo(Gropeeeardds.. - R)*
Advantages +
RB-R for each Rg e GroupRewards
ston
g=1
/ Policy update following GRPO Shao et al. 2024b Te f update na using GroupRollouts and Advantages
Output: Leamed policy model
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B TRAINING DETAILS
Parameter
Value
Base model
Qwen2.5-VL-77B-nstee
Data
V* training set, ArxivQA
Hardware
NVIDIA H100
Optimizer
AdamW
Total training steps
250
Global batch size
128
Rollouts per sample
8
Learning rate
x 10-6
RL algorithm
GRPO
Reward model
Qwenn-5-VL-77B--nnttrrtt
GPU hours
~50
Table 7: Training configuration.
Table summarizes the training setup.
Models. We adopted ( Instruct (Bai et al., 2025) as the base model for RI training, owing to its strong vision-language reasoning ability and object-level referring detection, which enable effective grounding without cold-start initialization. Moreover, Qwen2.5-VL. has been widely used prior RL-based studies, ensuring fair comparison with related work. For the reward model, of the most powerful open-sourced LVLMs, to provide
used -L--2---VV-22--Im reliable and precise reward signal.
Data. We followed the setup of DeepEyes (Zheng al. 2025b), reusing their curated training data for RL post-training. This choice isolates the contribution of our method from dataset curation ef- demonstrate improvements independently of data filtering, though such filtering
fects, allowing remains a valid and complementary approach.
Other training details. Training was performed cluster node with 4 H100 GPUs. The global batch size was 128. For accuracy rewards, half of each mini-batch was allocated to longer rollouts avoid VRAM bottlenecks. Sixteen rollouts rate was fixed at 10 throughout training. We employed standard GRPO, as alternative vari- ants such DR. GRRP Liu al. (2025) and GSPO (Zheng al.) (025aa) did not yield significant improvements in preliminary trials.
sampled per training example. The learning
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C ANALYZING LATENCY-PERFORMANT TRADE-OFFS BEYOND FIXED PIXEL CONSTRAINTS
We conducted additional experiments to broaden the scope of evaluation. Beyond the fixed pixel-constraint setting, we evaluated two supplementary comparisons: one under performance- constrained settings, where each model evaluated using its native configuration, and another un- der latency-constrained settings, where all models operate within comparable inference-time limits. These additional tables provide more complete view of ERGO's performance across different prac- tical constraints. All experiments are conducted using the vLLM engine (Kwon et al.) 2023 on single H100 GPU with batch size of 16.
Model
V
Latency (s)
Remarks
TGR
85.3
5.3
Original setting
DeepEyes
84.3
9.4
Original setting
Mini-o3
86.8
9.0
Original setting
ERGO
85.9
Pixel constraints: 2256 x x
ERGO
83.8
2.6
Pixel constraints: 1280 x 28 x 28
Table 8: Comparison under original baseline settings without restricting baselines" configurations.
Table B shows the results under the native configurations the models. When computational lim- its are relaxed (e.g., through larger pixel budgets), ERGO matches or exceeds the performance of stronger but slower baselines, while maintaining lower latency.
Model
V*
Latency (s)
Remarks
VrR
74.4
2.6
Pixel constraints: 2048 28 28
81.7
3.1
Pixel constraints: 2560 28 28
DyepEyes
64.9
3.2
Pixel constraints: 640 x 28 x 28
75.9
3.8
Pixel constraints: 1280 28 28
Mini-o3
74.4
2.6
Single tool call; Pixel constraints: 1280 28
28
74.4
5.0
Single tool call
83.8
7.0
Up to tool calls
ERGO
83.8
2.6
Pixel constraints: 1280 x 28 28
Table 9: Performance under matched latency budgets by regularating pixel constraints or tool call counts.
To evaluate models in latency-sensitive environments, we further cap inference time per sample and tune pixel budgets or permissible tool-call counts accordingly. As shown Table 9 ERGO achieves the highest accuracy among all models when operating under comparable latency budgets, demonstrating its feffciency-peffommee advantage in constrained settings.
17

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Published as a conference paper at ICLR 2026
D
QUALITATIVE RESULTS
The qualitative results indicate that ERGO effectively leverages coarse visual cues when needed.
Resized from original image (Pixel Constraint: 1280x28x28)
Question
What is the color of the bottle cap?
First, consider how zoom in the image to include only the region containing all the information necessary to answer the question.
Reasoning
To determine the color the bottle cap, need to focus on the region where the bottle located. The bottle is visible on the table in front of the person wearing plaid shirt.
Zoom in on the table in front of the person wearing plaid shirt to focus on the bottle
Cropped at original image (Pixel Constraint 1280x28x28)
Question
What is the color of the bottle cap?
Reasoning
The bottle cap is located on the water bottle next to the person reading. Upon zooming in, the cap appears to be blue.
Answer
Blue.
Figure 10: ERGO utilizes coarse cues ('the region where the bottle is located"") to provide the answer. The yellow box highlights the region linked to the answer for clear visualization.
18

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Published as a conference paper at ICLR 2026
Resized from original image (Pixel Constraint ::::::220: 1280x28x28)
Question
What is the color of the pink-haires woman's 's handbag?
First, consider how to zoom in on the image to include only the region containing all the information necessary to answer the question.
Reasoning
To determine the color of the pink-haired woman's handbag, need focus on the area near the entrance of the building where people are visible. The region around the entrance contains the necessary information.
Zoom in on the entrance area to focus on the people near the building.
HON SOFT
Cropped at original image (Pixel Constraint 1280x28x28)
Question
What is the color of the pink-haired woman's handbag?
Reasoning
The enlarged sub-image focuses on the entrance area where a person with pink hair is visible. The handbag she is carrying appears be white.
Answer
Whitc.
Figure 11: ERGO leverages coarse cue ('where people are visible"" to answer the question. The yellow box indicates the region associated with the answer to aid visualization.
19

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Published as a conference paper at ICLR 2026
Resized from original image (Pixel Constraint 1 1280 28
Question
the orange luggage on the left or right side of purple umbrella? First, consider how to zoom on the image include only the region containing all the information necessary answer the question.
Reasoning
To determine whether the orange luggage is on the left or right side of the purple umbrella, need focus on the area around the umbrella and the luggage. The umbrella is located near the center-left of the image, and the orange luggage visible the right of the umbrella.
Zoom on the area around the purple umbrella and the orange luggage.
Cropped at original image (Pixel Constraint 1280x28x28)
Question
the orange luggage on the left or right side of the purple umbrella?
Reasoning
The enlarged sub-image focuses on the area around the purple umbrella and the orange luggage, making clear that the orange luggage is positioned to the right of the umbrella.
Answer
Right.
Figure 12: ERGO can also exploit clear visual cues (the purple umbrella and the orange lug- gage) when the object is still discernible. The yellow box highlights the region associated with the answer for clear visualization.
E LLM USAGE DISCLOSURE
This paper utilized a large language model (LLM) solely for the purpose of checking grammar, spelling, and typographical error..
20


Reviews:
[{"review_id": "zOg0rJ8nri", "reviewer": "Reviewer_zSHE", "summary": "This paper presents ERGO, a model designed to tackle high-resolution visual understanding for vision-language models without burning through too much computing power. The core idea is a two-stage pipeline: first, it analyzes a low-res version of the image to spot task-relevant regions, then crops only those areas at full resolution for detailed reasoning. Unlike other methods that struggle when objects get blurry after downsampling, ERGO uses reasoning-driven perception to find important regions. Trained with a reinforcement learning framework that includes rewards for good region selection, efficient crop size, and accurate answers, ERGO delivers impressive results. It outperforms models like Qwen2.5-VL-7B on various benchmarks. Even with strict pixel constraints, it maintains high accuracy across multiple high-res and conventional multimodal benchmarks.", "questions": "As stated in weakness part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The reasoning-driven perception approach is super smart. By using context to find relevant regions instead of relying on clear visual cues, it handles blurry low-res inputs way better than existing perception-driven models. \n\n2. The authors propose a well-designed reward system in the RL framework, combining rewards for region quality, crop size, and answer accuracy, makes sure the model learns to be both effective and efficient. \n\n3. The results are really practical. The model delivers real latency improvements and doesn’t lose out on performance when applied to other multimodal tasks beyond high-res reasoning.", "weaknesses": "1. The author mentions using a frozen reward model to provide the region reward. Analysis of how the reward model performs is crucial for others to validate the effectiveness of this reward design.\n\n2. I appreciate the authors' effort in providing the predicted area ratio. However, I also want to know the success ratio of the predicted area for benchmark evaluation. This serves as an important metric to analyze how the model perform and the effevctiveness of model training.\n\n3. The authors should provide more baseline method, like using the SFT method to performance area cropping in previous work (Visual-CoT and Chain-of-Spot), also the zero-shot baseline and tool-calling baseline should be included.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "O2Uw5ufC6s", "reviewer": "Reviewer_6jTD", "summary": "The authors introduce ERGO (Efficient Reasoning & Guided Observation), a novel rl-based framework for efficient high-resolution visual question answering in VLMs. The key innovation lies in a reasoning-driven perception pipeline that identifies semantically relevant regions using low-resolution inputs and re-encodes those regions at full resolution for fine-grained reasoning. Unlike traditional perception-driven approaches, ERGO explicitly integrates multimodal reasoning into region selection through a two-stage coarse-to-fine pipeline and a task-driven contextual exploration reward in the RL setup. Experiments across multiple high-resolution VQA benchmarks demonstrate strong performance and efficiency, outperforming state-of-the-art models in both accuracy and computational cost.", "questions": "See Weaknesses Above.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Introduces a principled RL framework that aligns vision token efficiency with VQA performance using a custom TCE reward.\n2. Paper is well-written, figures are illustrative, and reward function formulations are clear .", "weaknesses": "1. While the RL approach is justified, comparison with alternative non-RL cropping or saliency-based models is missing.\n2. Qwen2.5 is used as both the base and reward model. It's unclear whether the approach generalizes well to other vision backbones.\n3. The paper assumes that an RL formulation is the optimal paradigm for guiding region selection, yet no strong justification is provided for why this should outperform supervised learning approaches (e.g., using ground truth regions, attention supervision, or self-training).\n4. The proposed reward structure (especially the region-verification reward) seems to mimic supervised alignment between image regions and answers, raising the question of whether RL introduces unnecessary complexity.\n5. The combination of region-verification and box-adjustment rewards appears empirically effective, but the design choices (e.g., fixed $\\gamma$ = 0.6, hard step function in Eq. 2) are purely heuristic.\n6. The core contribution is the “reasoning-driven perception” strategy, yet the evaluation focuses heavily on end-to-end VQA accuracy, without thoroughly validating whether the selected regions are actually semantically aligned with the question (e.g, via IOU with GT regions or attention overlap with human fixations). Figure 5 attempts to address this, but the metric is poorly defined, lacks baseline comparisons, and only handles binary masking scenarios.\n7. The reward model is frozen, but still based on Qwen2.5-VL-72B, which is the same family as the base policy model (Qwen2.5-VL-7B). This introduces a risk of reward leakage, particularly if the reward model and policy share weights or similar training objectives.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FyaQg7owQV", "reviewer": "Reviewer_uFQT", "summary": "This paper introduces ERGO (Efficient Reasoning & Guided Observation), a two-stage \"coarse-to-fine\" framework aimed at improving the efficiency of high-resolution image understanding in Large Vision-Language Models (LVLMs). While existing two-stage methods often fail when target objects are indiscernible in low-resolution inputs, ERGO solves this by transitioning from \"perception-driven reasoning\" to \"reasoning-driven perception,\" which uses contextual information to locate relevant image regions.\nTo achieve this, the authors employ a reinforcement learning (RL) scheme and propose the Task-driven Contextual Exploration (TCE) reward. This reward includes a component that ensures the cropped region is self-sufficient for answering the query, and another that actively encourages smaller, more efficient crops. Experiments on high-resolution benchmarks, including V* and HR-Bench, show that ERGO achieves superior accuracy while drastically reducing the number of vision tokens used, leading to faster inference compared to the base model and competing efficient methods.", "questions": "Q1. Why is the Performance Gain So Small on HR-Bench 4K? According to Table 2, under the 1280×28×28 constraint, ERGO's gain on HR-Bench 4K is much lower than its gain on HR-Bench 8K and V*. Could the authors provide a fundamental analysis to explain why ERGO's advantage is significantly suppressed in the HR-Bench 4K scenario?\n\nQ2. Performance and Efficiency Comparison without Resolution Constraint. If the initial input resolution were relaxed to be close to or equal to the original resolution, how would ERGO's performance and efficiency be affected?\n\nQ3. Testing Generalization on Complex High-Resolution Tasks. The high-resolution benchmarks evaluated in this paper, such as V* and HR-Bench, are relatively simple. Given that the coarse-to-fine strategy might be more effective in simpler scenarios, could the authors provide performance data for ERGO, including a comparison against relevant works (DeepEyes, PixelReasoner, TreeVGR, Mini-O3), on more complex benchmarks that require multi-step reasoning, logical judgment, and finer targets (e.g., Visual-Probe[1] or TreeBench[2])?\n\n[1] Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search\n\n[2] Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology\n\nQ4. Quantifying the Multi-Turn Inference Cost. ERGO's inference process is essentially a multi-turn tool calling process. Could the authors compile and report the average number of tool calls per sample on the high-resolution benchmarks, along with a comparison to relevant works, to quantify the overhead of the decision-making process?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Solves a Real and Critical Bottleneck: The paper tackles a major pain point: the prohibitive cost of processing high-resolution images for LVLMs in real-world applications. The authors sharply identify the failure case of existing coarse-to-fine methods—where tiny objects disappear after downsampling, and offer an effective, targeted solution.\n\n2. Strong Experimental Results and Efficiency Gains: ERGO not only achieves sota performance on several high-resolution benchmarks like V* but also dramatically cuts down the computational cost (fewer Tokens, less latency).", "weaknesses": "1. The complexity of the coarse-to-fine pipeline is increased by multiple tool calls. The authors do not provide the average number of turns per sample, which is a critical missing metric for a complete evaluation of the true inference efficiency.\n\n2. The Box Adjustment Reward uses a hard, dataset-derived threshold ($\\gamma$) to prioritize small crops. This rigid prior risks confining the model's exploratory behavior and severely limiting its generalization ability on scenarios where a large, global context is genuinely required.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0vVD9HXulp", "reviewer": "Reviewer_9PNo", "summary": "Directly processing high-resolution images increases computational overhead, as many processed tokens are not task-relevant. To address this, the paper proposes a two-stage, coarse-to-fine approach. In the first stage, the model processes a low-resolution image to identify task-relevant regions; in the second stage, cropped high-resolution images of these regions are used for further processing. To enable this mechanism, the authors combine standard accuracy and format rewards with a box adjustment reward and a region-verification reward. As a result, the model achieves better performance than the base model while processing significantly fewer image tokens.", "questions": "What causes the significant drop in DeepEyes’ performance as reported in the table 2?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper is well motivated, and the two-stage coarse-to-fine approach makes sense and appears effective.\n\n2. The model performs better than the base model while processing fewer image tokens.\n\n3. The motivation for the additional rewards, such as the region-verification and box adjustment rewards, is clear.", "weaknesses": "The system appears complex due to the use of multiple rewards and reward models.\n\nThere is no ablation study on the weights of the different rewards. For example, in Line 216, the paper directly specifies the weights without any explanation or ablation analysis.\n\nSome experimental details seem unclear. For instance, the paper lists DeepEyes [1] as a baseline for comparison, but the reported evaluation metrics differ significantly from those in the original DeepEyes paper.\n\nReference:\n[1] DeepEyes: Incentivizing “Thinking with Images” via Reinforcement Learning", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)