ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
Authors: Xiyao Wang
Zhengyuan Yang
Chao Feng
Yuhang Zhou
Xiaoyu Liu
Yongyuan Liang
Ming Li
Ziyi Zang
Linjie Li
Chung-Ching Lin
Kevin Lin
Furong Huang
Lijuan Wang
Venue: NeurIPS 2025 poster ()
Keywords: Visual reasoning; Vision-Language Model; Visual captioning; Reward Model; Visual Hallucination
Primary area: applications

Abstract: Reinforcement learning (RL) has shown great effectiveness for fine-tuning large language models (LLMs) using tasks that are challenging yet easily verifiable, such as math reasoning or code generation. However, extending this success to visual perception in vision–language models (VLMs) has been impeded by the scarcity of vision-centric tasks that are simultaneously challenging and unambiguously verifiable. To this end, we introduce \textbf{ViCrit} (\textit{Visual Caption Hallucination Critic}), an RL proxy task that trains VLMs to localize a subtle, synthetic visual hallucination injected into paragraphs of human-written image captions. Starting from a 200-word captions, we inject a single, subtle visual description error—altering a few words on objects, attributes, counts, or spatial relations—and task the model to pinpoint the corrupted span given the image and the modified caption. This formulation preserves the full perceptual difficulty while providing a binary, exact-match reward that is easy to compute and unambiguous. Models trained with the \textbf{ViCrit Task} exhibit substantial gains across a variety of VL benchmarks. Crucially, the improvements transfer beyond natural-image training data to abstract image reasoning and visual math, showing promises of learning to perceive rather than barely memorizing seen objects. To facilitate evaluation, we further introduce \textbf{ViCrit-Bench}, a category-balanced diagnostic benchmark that systematically probes perception errors across diverse image domains and error types. Together, our results demonstrate that fine-grained hallucination criticism is an effective and generalizable objective for enhancing visual perception in VLMs.

for fine-tuning ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
Xiyao Wangl.2t, Zhengyuan Yang?tv, Chao Feng3.4f
Yuhang Zhou', Xiaoyu Liu Yongyuan Liang' Ming Li', Ziyi Zang
Chung-Ching Lin Kevin Lin Linjie L Furong Huang", Lijuan Wang
University of Maryland, College Park "Microooft
University of Michigan *Cornell University CCrdiiff University
First Authors 'Equal Advising Projecct Lead
xywangSumd. edu zhengyangdmicrosoff
Abstract
Reinforeement learning (RL) has shown great effectiveness for fine-tuning large language models (LLMs) using tasks that challenging yet casily verifiable, such as math reasoning or code generation. However, extending this success to visual perception in vision-language models (VLMs) has been impeded by the scarcity vision-centric tasks that are simultaneously challenging and unambiguously verifiable. To this end, introduce ViCrit (Visual Caption Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle, synthetic visual hallucina- tion injected into paragraphs of human-written image captions. Starting from 200-word captions, we inject single, subtle visual description error-altent arror-alterig a few words on objects, attributes, counts, or spatial relations and task the model to pinpoint the corrupted span given the image and the modified caption. This formulation preserves the full perceptual difficulty while providing binary, exact- match reward that is easy to compute and unambiguous. Models trained with the ViCrit Task exhibit substantial gains across variety VL benchmarks. Cru- cially, the improvements transfer beyond natural-imas image training data to abstract image reasoning and visual math, showing promises of learning to perceive rather than barely memorizing seen objects. To facilitate evaluation, we further intro- duce ViCrit-Bench, a category-balance probes perception errors across diverse image domains and error types. Together, our results demonstrate that fine- gra-ggr hallucination criticism is an effective and generalizable objective for enhancing visual perception in VLMs.
diagnostic benchmark that systematically
Introduction
Reinforcement learning (RL) has recently emerged as a dominate paradigm 17 23
large language models (LLMss) when training tasks are both challenging and automatically verifiable. Successful examples include mathematical reasoning tasks with concise numerical answers [9 and software engineering problems [78, 38] whose correctness be checked in sandboxed environment. By focusing on tasks that strike this balance- balance-suuffiiient challenging to have room for improvements yet straightforward to grade deterministicall--RR can explore the solution space effectively, extract genuinely useful strategies, and transfer those gains to broader domains.
Despite its success in textual reasoning, RL training with verifiable rewards has yet to demon- strate a comparable significance improving the visual perception abilities lision-language models (VLMs). This is largely due to the lack of vision-centric tasks that are both perceptually
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Synthetic Hallutination Injection
VLM RL. via Vicrit Taek
Object pisoriiion Pertubation
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ViCrit Task: Hallucination Prediction
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Figure I: Overview of the ViCrit framework. Starting from high-quality caage-capiion pairs, we synthetically inject visual hallucinations by minimally altering noun phrases. The model is trained to localize incorrect spans in the caption given the image, receiving a verifiable reward through exact string matching. This fine-grained perceptual objective improves visual perception in vision-languea models (VLMs) and generalizes to downstream reasoning tasks across diverse visual domains.
challenging and automatically gradable. Whereas multi-hop math problems naturally compress numerous premises into a single verifiable answer, the semantic elements within an image rarely collapses into such a tidy question-answer pair. Even advanced visual-question suatalluussioo-asveriiii answering bench- marks 21 51 35 often probe only fragments of scene, allowing shallow perception to suffice. Attempts to increase the perceptual difficulty, such as exhaustive image captioning that enumerates every visual element 45| 12 29 2 65 yield paragraph-length outputs (200+ words) that are nearly impossible to grade unambiguous gunnli. The central challenge, therefore, to craft a task that forces the model to perceive the full scene yet produces a concise, deterministically verifiable response.
To bridge this gap, we propose ViCrit (Visual Caption Hallucination Critic). a reinforcement learning proxy task that offers both perceptual difficulty and evaluation simplicity. ViCrit trains VLMs to localize synthetic visual hallucinations injected into paragraph-length image captions. It is designed to be both challenging, requiring fine-gre -re-grri visual perception, and verifiable, enabling deterministic reward signals for scalable RL training. As shown in Figure [] the task begins with detailed image captions with more than 200 words [12]. and synthetically injects visual hallucinations. Such subtle errors misdescribe object, attribute, count, scene text, or spatial relation as its visually similar alternative. The model is trained to act as critic: given an image and its corrupted caption, it must identify the specific tokens that are incorrect. This token-level span detection can be easily graded via string matching yet requires fine-grained visual perception across the entire image, encouraging the model to internalize robust visual perception strategies extracted during the RL exploration trajectories.
Training Qwen2.5- S--VL-LB-IIISEEE and 72B-Instruct with the proposed ViCrit RL, task yields con- sistent gains across ten vision- languaee benchmarks. In addition to better hallucination benchmark results, these improvements extend well beyond the natural- image domain seen during ViCrit RL training, onto abstract image reasoning and visual math: Qwen2.5-VL-72DD --2-----rc- improves from 35.2% to 40.1 1%% on Math Vision [57], from 61.3% to 65.8% on VIMsAreBlind [46), and from 45.5% to 49.4% on Charxiv (63] These cross-domain improvements indicate that the learned perceptual strategies transfer effectively to general VL domains. By training models to pinpoint fine-grained errors, ViCrit encourages the development of internal perception strategies that cross-cher check textual claims against visual evidence. Unlike supervised fine-tuning on captioning data 49 [5] 54], which can lead to surface-level memorization, our RL task rewards perceptual correctness and penalizes hallucinations directly. As a result, the model moves beyond merely memorizing the seen object lists, towards learning to decide how perceive an image. Comprehensive analyses on how ViCrit- induced chain-of ohotthsshsss generalize to a broad spectrum of downstream VLM tasks further reveals the effectiveness and working mechanism of the ViCrit RL training.
In addition to ViCrit training, we present benchmark named ViCrit-Bench for evaluating VLMss on hallucination detection. We group images into four categories and hallucination types into eight fine-

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grained hallucination classes, enabling detailed diagnostic analysis. We then manually curate a set of images selected from PixMo-Cap [12] and inject eight types of hallucinations into their corresponding captions. This process results in a high-quality, fine-grained, and highly challenging hallucination detection benchmark, containing 607 samples. The benchmark supports zero-shot evaluation and exposes clear correlations with downstream perception tasks, making it a powerful probe of VLMs' perception limitations. We benchmark a range of state-of-the-ar open-source and closed-source vision-language models on HCCrit-Bench Even proprietary systems such as OpenAl-o3 and Gemini- 2.5-Pro achieve only 47.7% and 45.2% accuracy. After in-domain reinforcement learning with the ViCrit task, Owen2.5-VL-722 attains an improved accuracy of 43.0%. The gains are uniform across all four image categories and are especially pronounced on document and abstract images, highlighting the efficacy of ViCrit-based RL for strengthening generalizable visual perception.
Our contributions are summarized as follows:
We introduce ViCrit, an RL task for visual perception that requires VLMs to identify token-level visual hallucinations in paragraph-length image captions. The task is both perceptually challenging and automatically verifiable, enabling scalable RI. training with precise, unambiguous supervision.
Training VLMs with the ViCrit Task significantly enhances their performance on wide range of VL benchmarks. The improvements also generalize to other image domains such as abstract image reasoning and visual math, which shows the advantage of ViCrit incentivizing models verify visual detail against text, rather than merely memorize seen objects.
We present ViCrit-Bench that systematically probes eight hallucination types across four image domains. The benchmark supports zero-shot evaluation and serves as diagnostic tool for assessing fine- ane-grained visual perception capabilities in VLMs. Furthermore, its scores track averaged VLM accuracy monotonically, making it strong indicator of the overall VLM performance.
2 Related Work
Large language model reasoning. Prompting-basee Chain-of-thought methods 64 25
first ex-
plored the reasoning abilities of large language models (LLMs) [5] 10) by eliciting chains of in- termediate thoughts, markedly improving arithmetic and commonsense benchmarks [|| 43 26). Subsequent decoding strategies aim to further improve test-time performance with extra test-time computation. For example, Self-Consistency sampling [62] that votes over multiple thought paths to boost reliability. Expanding beyond linear traces, structured search frameworks like Tree-of- Thoughts 71 and Graph-of- Thouuhhss 24 let the model explore a branching space of candidate "thought" states before committing to an answer. Studies [39 also explore hacking the thought process to generate long CoT that is beyond the CoT length distribution. Moving from test-time scaling to training, process reward models 30 55 588 grade each reasoning step rather than only final answers, which can be coupled with Monte Carlo Tree Search 67 for fine-grained value estimates.
Most recently, large-scale reinforcement learning I7 23 with outcome- based rewards alone can induce emergent multi-step reasoning skills. The key to its success is challenging tasks that can be automatically verified, such that the RL can be effectively scaled up with minimal noise in its reward signals. The goal for this study is to find such tasks for VLMs' visual perception.
VLM reasoning. Based on the modern vision language models 42
56
22 31
53 28, 66]
recent studies explore the use of multimodal CoT to further improve vision- language reasoning tasks [8
57 33
72 with both grounded textual thoughts 33 77 and multimodal thoughts [48] 66
16. Techniques like rationale distillation and self-reflection further boost these models' reasoning
outcome-based reward based 37 36 (60, 44 6., 79 40) applics similar techniques to visual math and other visual- lisual-question-ttt wectiionnnnseemeee answering benchmarks. Despite the improvements in visual math and STEM questions, they still fall short of significantly advancing fine-grained visual perception.
capabilities 176
70 81
59 68
61
14] Inspired by the success
RL in LLMs, recent studies [3
20,
Visual-centric VLMs and reasoning, One threads of works aim to improve visual perception in VLMs via lext-supervised visual representation learning [49
15| [54) which trains the model to gener- ate good description of the image. This line of work show great promises with the recent success in
obtaining alsra-descriptive image captions 45 12 29 7 65]. However, supervised fine-tuning
on captioning data may lead to superficial object memorization, while paragraph captioning task does not have reliable rewards for RI. training. In this work, we present an RL. proxy task to close this gap.
3

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ViCrit Task: Which noun phrase is replaced by the hallucinated noun phrase in this image description?
Perturbed caption: [250 words)
The image cantures dets let nwn stew of gmy and while grankin or form ca taclerp Centrol to the compodtion mmartt the botton is rechnguiee yellow stynttum tmy communyy found in grosery sfor
Socuef
seatly
Caroll
email back jor abwild and Swary Meckunt in red and while betweing pootiore powen to thit a redicapped contaiser with latel meding "Paiknin Crocle feccecning" placed prominerily heat coft corner the tryy large bettle of Tiees's Poettrrr or Wing Souree updigt
scige
backgmund sightly the louch The dog saeem a aign seound it neck, aboet unmeddatle,, and is fartied sot aidd pepper aleccere neeted in Sity ornate dre benklls.
dhvrringg small abstus of brosnn tkeg adde, unimaace
imaea
aecurd
Ihe while lecel seaturing "Warks Star Cwrt fiee Good Stulf Honey Barbweusa while agaenst tan tackground comcleses the soone with hin of delectable flavor
Visual ination (0 words)
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n Hospital FRIDAY NOTINNITEETNN LIVE Luthe
the oustorr phoregraph musical band perlorming twneath a sih fecitol wit with open walls,
sipported by glay atrute and make suppora A prons nwnt blacs henter with the sed "Tisdyy Nights Low'
pank, witiee and yelrne theplioee
trent of the lect The zarmerr also leaturee logos and the word The herosee Speriihhle indudding crarge
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wheeing
(gey longee seipedeeeed start and blke jearss plays gaéden-csiooed becorchone, Bleate her, to the right, the lead singer stance front and certer dhessed antindy track with black lecors and a lighter gray bel: holte temboaanne htp. right brack billed beswesel guiaar the deep background hoodlo and blue jears es, her role the band untetemmiedd There are also track music stands placed around the musictons
maneghone
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open
green botton of photogrph "Chuck Willie *
sockground,
slear
the horizon
Visual hallucination (8T): (2 words)
Chucs NEIM
Figure 2: Instead of asking the model to write a paragraph-long caption that is hard to grade (e.g.. the 200-word example above). ViCrit feeds the model an almost-correct caption containing a single, deliberately inserted visual hallucination and trains it to locate that error. The short, token-level response is just as demanding in terms of visual perception, yet is far easier to verify automatically.
3 ViCrit RL Training
Recent progress in outcome-based reinforcement learning shows that LLMs learn richer reasoning procedures when trained with hard questions whose answers can be unambiguously verified. The same recipe, however, is not immediately available to visual perception in VLMs. The traditional caption-supervision objective optimizes a model for recalling a fixed list of objects, but never for deciding where to look next. Our goal is therefore to turn visual perception into an RL problem whose reward (i) compels the model to interrogate every visual details and (ii) remains as cheap and deterministic to evaluate as in code or math.
Examples of our proposed ViCrit task is shown in Figure 2 Instead of asking a model to generate a perfect, paragraph-length caption (200+ words), which is difficult to grade, we present it with an almost-correct caption containing single, synthetically injected visual error and reward the model for pinpointing the mistaken span (2 words). Solving this task is as hard as perfect captioning: critic that can reliably spot any hallucination must perceive and understand the entire scene; yet the answer collapses into few words that can be matched exactly. This simple reshape of the objective delivers the two missing ingredients for perception-centric RL: a genuinely challenging perception task and an evaluation rule that reduces to simple string equality.
3.1 ViCrit Task
Task description. For every training instance we start with an image I and its exhaustive, human-annotated caption C' drawn from the PixMo-Cap dataset [2). with average caption length of 196 words. We then prompt GPT-4 [42 to select one object description o within that w00-word-lengtt paragraph and perturb it into visual hallucination o, such that the perturbed object is visually similar, semantically plausible, and without ambiguity. We also sample two examples from small set of manually crafted in-context examples when prompting the LLM. The complete prompt is in Appendix. The desired output is minimally modified caption Ö that differs from C by exactly one visual span (e.g., two words in Figure 2 We instruct for diverse types of selected objects o and resulted hallucination o, such object substitution, attribute flip, scene-text error, relation swap, etc..
After data generation we task the model to identity the visual hallucination given the image / and caption C. A positive reward is given if predicted words matches ground-trutte ö. Because the reward depends purely exact string match, it is deterministic and easy to scale in RL training.

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Discussion on task difficulty. Perfectly performing the ViCrit task requires the model to perceive the entire visual scene, which is the same level of visual perception demanded of an "oracle" visual captioner that can exhaustively describe every image elements. Indeed, flawless ViCrit critic could be repurposed into such an oracle by iteratively proposing refinements to an image caption. Thus, ViCrit imposes the same visual perceptual requirements as paragraph captioning, yet its single-span output is easily verifiable, enabling scalable outcome reward based reinforcement learning.
Data. We build the image caption Ö starting from all samples in the PixMo-Cap dataset 12] Filtering out the invalid image URLs yields 384K image caption pairs. We then prompt LLM to create the visual hallucination and use to replace the original object description o create the minimally modified caption C. In the end, we collect 875K pairs of images and modified captions.
3.2 Model Training
We use Qwen2.5-VLL VL as the base VLM for experiments and finetune all model parameters via the ViCrit RL proxy task. We train the model with Group Relative Policy Optimization (GRPO) [50]:
LRROO = B.[min(A Ae, Pc, A, clppi((pp: 1 e, 1 1.1++) +)]];
No(p. x) A, = (r: F), Pi Te (Mh (z)
The sample reward is computed based on a deterministic string matching between the injected
[+1, if =-ō=-ō ô, We relax the
visual hallucination string o and the model prediction ò: Tanswer 0, otherwise.
string matching such that the model is not penalized for copying additional words before or after the selected span o, as long as they are an exact copy from the original caption C. In addition answer correctness reward, we also follow the standard practice to instruct the model to follow a specific prompt format, which group thoughts with special tokens <think?.. ... </think> and final answers with special tokens (b.xed The format reward F'ormat is I if it correctly uses the special format tokens and 0 otherwise. The final reward for sample is r; = 0.9 Tanswer 0.1 * I' format-
ViCrit Benchmark
Motivated by the substantial gains yielded by reinforcement learning with the ViCrit task, hypothesize that zero-shot ViCrit accuracy also correlates with a VI.M's perception capability and can therefore anchor diagnostic benchmark. We thus present ViCrit-Bench, high-quality, fine-grained, and highly challenging benchmark for hallucination detection. In this section, we first present the image domains and hallucination task categories defined in ViCrit-Bench. We then describe the human annotation procedure and dataset construction pipeline. Finally, we provide comprehensive statistics and distributional insights of ViCrit-Beneks
4.1 Image Domains and Hallucination Categories
ViCrit-Bench partitions its images into four broad domains, each chosen to probe a complementary slice of visual perception: (1) Natural images: everyday photos of landscapes, animals, people, and objects captured in the wild; (2) Documents: images dominated by structured content such as tables, charts, plots, diagrams, or dense textual screenshots; (3) Scene-fext-heavy images: images where scene text is appeared in the scene, such as street signs, memes, comic panels, and illustrative layouts; (4) bstract images: images that do not directly depict real-world objects or scenes, but instead convey meaning through geometric shapes, symbols, color patterns, synthetic compositions, or artistic illustrations; these images emphasize structure, style, or conceptual abstraction rather than natural realism or textual content.
We note that the training data distribution from the PixMo-Cap dataset contains dominated natural images. The annotation on a subset of 4k randomly sampled Pix -ap images shows a category percentage of 59%, 10%, 24%, and 7% for image domains 1 1-4, respectively. Human annotators thus go through additional candidate images to find the proper sources for categories 2 and 4. Based on the image domains above, we then systematically categorized all visual hallucinations into eight distinct hallucination task types, defined as follows: (1) Count: evaluates whether the quantity of objects or elements is incorrectly described; (2) Material: assesses the model's 's ability to accurately recognize the material composition of objects; (3) Spatial: determines whether the spatial configuration and
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1. Natural Image
2. Document
3. Scene Text Heavy Image
4. Abstract Image
Caramel Apple Cider
Category: Color Originall yeloww plates orange lonse plates
Catagory:
Catagory
Original:
Origina largu, bloody sword
Category. loxt
paper cup
Original Nice Air More Uptempo
Halucination: a soldd white glass cup
Halluchation largo Lloody crutch
Halluchation: Nike Air More Upttempo
Category: Spacial Original uppar gl green follage
Category Condition Original
Count Origiaal
Category
Category: Shape
goes up
pillars aloven red pillas
Original yellow rectangle
Hallucinatio:: upper right, glimpse green foriage
Halluchation: aroo
Halluciatioo:
Hallcination a yellow ellipse
thai goes down
Figure 3: Data examples from ViCrit-Bench, which involve four image categories and eight visual hallucination types. We manually verify each image's 's long caption, and carefully inject different kinds of proper visual hallucinations.
relative positioning of entities are misrepresented: (4) Color: examines the consistency between the described and actual color attributes of visual elements; (5) Object: identifies cases where objects
are incorrectly classified into wrong semantic categories; (6) Condition:
checks whether the physical
state, dynamic action, or emotional expression of entities is appropriately conveyed; (7) Shape: measures the accuracy of describing the geometric structure or contour of objects; (8) Text: verifies whether embedded textual content within the image is correctly detected and interpreted.
4.2 Annotation Pipeline
All samples ViCrit-Bende originate from PixMo-Cap whose 200-word, detailed captions provide a fertile substrate for hallucination synthesis. The construction pipeline proceeds in three stages.
Stage 1: Image selection and caption sanitization. For the four image categories, first employ Al-o model to perform an initial classification over the entire PixMo-Cap dataset, identifying candidate images that align with the definitions of natural images, document images, scene-text-heavy images, and abstract images. Subsequently, human annotators manually filter the candidates and select 20 images for each hallucination task with each image category, ensuring images strictly adhere to the domain-specific criteria.
Besides, given that some image captions in PixMo-Cap contain annotation errors, first used o3 to review and automatically correct the captions, addressing the majority of semantic and factual issues. During the final annotation phase, each caption is individually reviewed and validated by human annotators to guarantee its accuracy and consistency. total of four human annotators are involved in this process.
Stage 2: Hallucination injection. Each image category is assigned to a dedicated annotator, who injects the selected hallucination types by surgically replacing single phrase. This is done by replacing noun phrases with semantically plausible but mislcading alternatives that are visually similar, shown in Figure 3 Each image is allowed to be modified with only one hallucination. For each image category, the number of images per hallucination task capped at 20. However, in certain categories, some types of hallucinations may be inherently example, material hallucinations abstract images-which which may result in fewer than 20 finalized examples for those specific tasks.
difficult instantiate- for
Stage 3: Cross-validation. A final round of cross- cross-valices validation by two independent annotator ensures the correctness and clarity of the injected hallucinations across all task types.

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Table I: Comparison between ViCrit-RL-7B and ViCrit-RI-72B with other open-source VLMs. After training on the ViCrit task using Qwen2.55-VL.7B-Inmmmme and Qwen2.3- 21..5---V-72B--mme Instrucct as base models, hallucination rates are significantly reduced, achieving the best performance across all three hallucination benchmarks. Moreover, training on the ViCrit task substantially improves general vision-language performance. On eight general VL benchmarks, ViCrit-RL-72B achieves SOTA results on seven tasks, with the average accuracy increasing from 59.78 to 63.16.
Hallucination Renchmark
CHAIRS
MattViiu
testiii
Matt Verse
XMM-V
Charsit
MANSA
Model
Avg.
GPT-4o
63.8
36.8
50.2
69.1
64.7
69.1
50.4
52.7
57.10
73.9
58.2
57.0
78.2
Molmo-7H-DD-9924
36.7
6.0
3.03
54.1
19.5
23.2
40.2
52.6
59.2
43.3
30.8
40.38
LLaVA-OneVisoon-T
35.0
5.5
3.12
63.2
17.4
26.2
48.8
61.7
57.5
40..1
31.3
43.28
InterVL2.5.88
Qwen2.5-Vi
2.om2.5-Vi-2B-nnmmmma
28.0
5.1
3.74
67.8
23.6
44.5
50.6
61.7
66.0
49.3
41.4
50.61
ViCri-R11-7B
25.2
4.5
3.77
70.7
25.7
46.3
52.0
61.9
67.1
52.6
47.8
53.01
(Ours Qwe32.5-78)
-2.8
-0.6
+0.03
+2.9
+2.1
+1.8
+1.4
+0.2
+1.1
+3.3
+6.4
+2.40
Molmo-728-092
28.8
24.7
30.9
48.3
65.5
46.9
LLaVA-One Visionnn-72
27.4
4.9
3.71
67.5
29.3
39.1
56.8
66.1
63.7
49.6
38.2
51.29
72.3
34.9
68.7
68.9
72.3
Qwen2.5 S-L-2..----22-Bmmmssss
26.4
4.8
3.82
74.8
35.2
53.3
63.4
68.4
76.3
61.3
45.5
59.78
ViCrit-RI- R1-1-R
21.0
3.9
3.91
77.3
40.1
59.8
66.0
69.8
77.1
65.8
49.4
63.16
Qwen2.5.72B)
4.3 Statistics
Co oliic
Following the aforementioned image selection and hallucina- tion injection procedures, the final ViCrit-Bench contains 607 images, each paired with manually verified and edited captions containing total of 607 fine-grained hallucination instances. The distribution of hallucination tasks across the dataset is illustrated in Figure 4 All hallucination task types, except for
13.2%
13.0%
let
13.0%
13.2%
Naterill
Material, are relatively balanced, each comprising around 13% the total instances. This reflects the comprehensive and well- balanced design of ViCrit-Bench. Due to its unique nature, the Material task appears mostly in first three categories, resulting in lower overall proportion of 7.9%.
13.3%
Quse
13.2%
Spatial
Figure 4: Hallucination task distri- bution of ViCrit-Bench.
4.4 Metric and Evaluation
For each sample, we combine the image / and corrupted caption Ö with predefined evaluation prompt template (see Appendix(A.2 (A.2) to form the final evaluation model input. Given this prompt, a VLM must locate the hallucinated span inside Ö as an open-ended QA task. prediction is considered correct if the model's prediction â exactly matches We take this string exact match accuracy as the metric for ViCrit-bench.
Experiments
5.1 Effectiveness of ViCrit RL Training
We evaluate the effectiveness of ViCrit-based RL on various VL benchmarks. Through extensive comparisons with SOTA VLMs, we demonstrate the effectiveness of ViCrit as an RL training task and show that reinforcement fine-tuning on this task leads to general VL performance improvements.
Baseline VLMs. We start from the Qwen2.5-VLL ----------ttuttu and Dwen2.5-VL-722-Iman Instruct check- points. Applying RL training with the ViCrit task produces our ViCrit-RL--B and ViCrit-RI-7215 respectively. Qwen2.5 models thus constitute our primary models of interest well as a fairly com- parable baselines for ablation. For external comparison, we report benchmark results for three widely used open-source VLMs: Molmo [12. LLa Ona-Onnniionn [27], and Inter VL2.5 19), including both
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their 7B-level and 72B-level variants. We also reference proprietary models include GPT-4o and ol. All training and evaluation is conducted with 8 A100 GPUs.
Evaluation benchmarks. (i) Hallucination mitigation: we first quantify ViCrit training's 's impact on dedicated visual hallucination benchmarks. (üi) Broad VIM generalization: we then examine if the perceptual skills instilled by ViCrit-based RL transfer to general vision-language benchmarks.
(i) Hallucination mitigation. We adopt two widely used benchmarks: CHAIR 47 and MMHal [52 Specifically, CHAIR quantifies the proportion of hallucinated content in image captions. Following the setting in previous works [80, [1], we randomly sample 500 images from the COCO Val2014 dataset and use prompts from the LLaVA- 150k detailed description dataset, and calculate CHAIR as Kballucinared objects
follows: CHAIR, = [[all mentioned objects] CHAIRs = |eaptions with hallucinated objects}| MMHal serves
[fall [[all captions]
as complementary benchmark for evaluating hallucination in VLMs VQA tasks. We employ GPT-4 as the scoring model to assess the hallucination severity in model responses.
(ii) Broad generalization. We use 8 widely adopted VLM benchmarks covering mathematical reasoning (Math Vista [33], Math Vision (57, Math
75
), general knowledge (MMMU 774)
MMStar 8 MM 72 73), visual understanding (Blind 46 and chart reasoning (ChartXiv 63
The middle three columns of comparess our ViCrit-RL with other VLMs visual hallucination benchmarks [ At the 7B scale, compared with the baseline Qwen2.5-VL-77 ViCrit-based RL training reduces CHAIRs and CHAIRi from 28.0 and 5.1 to 25.2 and 4.5, respectively. The MMHal increases to 3.77, which surpassing multiple 72B-level models. At the 72B scale, the improvement even more pronounced: CHAIRs and CHAIRi reaches 21.0 and 3.9, and MMHal improves to 3,91, outperforming all SOTA VLMs across all three hallucination metrics. The consistency of the improvements across scales and benchmarks validates ViCrit's effectiveness in reducing visual hallucination and improving perception across various description types.
Beyond curbing hallucinations, the right side of Table T shows that ViCrit-RL consistently lifts accuracy on the eight heterogeneous VLM benchmarks that constitute our "general vision-language" suite. Because the ViCrit proxy forces the model verify every noun phrase against the image, refines low-level perception and yields more faithful intermediate representations. These improve- ments appear to propagate to downstream reasoning tasks, with averaged improvement of 2.4% on 7B scale and 3.4% on 72B scale. More importantly, the improvements generalize well onto the low-source training image domains. For example, the 72B model improves +4.9% on Math Vision, +4.5% on VLMsareBlind and +3.9% on ChartXiv, despite math and abstract images only account for 7% of the PixMo-Cap training data, and 10% for chart and document images. This indicates that the model is not merely memorizing object lists but has learned a transferable strategy for "how to look" at an image before generating text. We provide a qualitative thain-of-thougeee analysis in Section 5.3 to probe this generalization pattern further.
5.2 ViCrit-Bench Results
We benchmark a broad range of SOTA VLMs our ViCrit-Bench, which probes eight fine-grained visual ballucination types across four image domains. For closed-source models, we evaluate OpenAl- GPT-series which includes 4o, ol and o3, and Gemini-series which includes 2.0-Flash, 2.5-Flash, and 2.5-Pro. For open-source models, we follow the same experimental setup as Section [5. and evaluate Molmo. LLaVA-One Vision, IL2.5 and Qwen2.5-VL -L series. Table 2 shows that ViCrit-Bench is markedly challenging: the best model o3 reaches only 47.7% ..7% correctness, while the best open-source model -wwn2.55-VL-72B-Imme
achieves 42.4%.
Spatial hallucination emerges as the dominant failure mode, with the sop-performing model achieving only 28 33.40 whereas object hallucination and material hallucination looks casier on paper. However, the higher number is because of an easier question subset on foreground objects, and it remains nontrivial to perform perfectly
any one of these eight classes (cf. the "corgi" object example in Figure 2 With respect to image types. "Document Image" and "Abstract Image" are the most challenging ones, as nearly all models exhibited significantly lower accuracy these two types compared to image types.
Furthermore, RL training with ViCrit task leads to substantial gains on ViCrit-Bench. ViCrit-RL-7B and ViCrit-RI-72B achieves an improved accuracy of 35.6% and 43.0%, respectively. Among four image categories, the largest gains occur on the Document and Abstract image domains, precisely the
8

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Table 2: Evaluation results of a range of closed-source and open-source VLMs on ViCrit-Bench. The results indicate that ViCrit-Bench poses a substantial challenge to current models even the best-performing model, OpenAT-o3, achieves only 47.7 accuracy. After reinforcement fine-tuning on the ViCrit task, -LC achieves the highest accuracy of 43.0 over all opensouce models on the benchmark. Moreover, we observe strong correlation between performance on ViCrit-Bench and the average accuracy on general -sion-language tasks for open-source models. Models that score higher on ViCrit-Bench tend to perform better on general benchmarks, suggesting that ViCrit-Bench serves as a reliable indicator of overall reasoning and understanding capabilities.
Hallucination Type
Image Type
Geneall Task
Condicion
Decument
Scone ToxE
Material
Abburacc Enitgge
Aug.
Object
Color
Count
Stapp
Models
Overall
OpeaAI-GPT-to
23.3
47.50
17.50
27.08
13.58
16.25
18.75
26.58
20.25
27.04
255
15:09
26.17
OpenAI-ol
48.10
40.51
32.91
42.14
47.26
OpenAl-o3
47.7
67.50
46.25
60.42
22.22
50.00
62.50
54.43
22.78
51.57
49.29
43.40
46.31
Deminii2.0-Flaa
19.3
30.00
50
59,58
6.17
16.25
13.75
18. 99
15.19
25.16
19.29
16.35
16.11
D.5-isi2...-Flash
41.25
47.50
32.91
44.65
40.94
Gemini-2.5.PP
45.2
68.75
50.00
66.67
20.99
43.75
50.00
39. 29.24
30.38
46.54
46.43
52.83
34.23
Molmo 7B. DB-049
40.48
9.5
25.00
8.75
10.42
9.88
5.00
3.75
6.33
7.59
5.66
13:57
8.81
10.74
JJaVi--OneVisinn-7
43.28
12.4
20.00
11:25
10.42
12:35
6.25
15.00
12.65
10.13
17.61
7.86
9.43
14.09
MM2.58--2
49.11
11.25
25.00
12.50
15.00
27.04
13.21
24.16
Qwen 2ecen2.55VL-72
50.61
21.9
30.00
23.75
39.58
9.88
12.50
8.75
45.57
12.66
35.22
12.86
20.13
18.12
ViCri- BBB
53.01
35.6
47.500
46.25
68.75
6.17
38.75
37,50
21.52
31.65
41.51
30,00
38.99
30.87
Molmo-72B
46,38
18.2
36.25
11.25
10.42
11.11
6.25
13.75
25.32
35.85
23.90
18.57
13.21
16.78
LLaVA OneVonnn
51.29
20.00
L7.50
21.25
22.78
24.53
58.75
32.7
46.25
21.25
45.83
22.22
20:00
26, 25
46.84
37.97
38.99
29.29
31.45
30:20
Qwea-2.5-VL-722B
59.78
42.4
57.50
36.25
58.33
28.40
26.25
46.25
54.43
37.97
47.17
40.71
44.03
36.91
ViCrit-RL-72B
63.16
|
43.0
60.00
48.75
70.83
17.28
40.00
56.25
25.32
39.24
47.80
41.43
44.65
37.58
areas where baseline models struggle. This improvement also foreshadows the model's enhanced performance on downstream benchmarks involving multimodal mathematical reasoning and chart understanding after ViCrit-based training, as shown in Table
However, we observe a significant drop in accuracy for the Spatial and Text tasks after RL training. We attribute this to data imbalance in the construction of the training set. Furthermore, RL training led to substantial performance gains for the 7B model on riCrit-Bench, whereas the improvements for the 72B model are relatively marginal. We hypothesize that this is due to the constructed training set being insufficiently challenging for Q2wen22.5-VL-777 which already possesses strong visual perception capabilities. To further enhance the performance of the 72B model, more complex and demanding data may be required.
Beyond the raw results, Table 2 exposes monotonic link between the ViCrit-Bench results and the general VLM performance. Models rank in precisely the same order in the ViCrit-Bence Overall column, as in the averaged VLM performance column quoted from Table I Figure 5 demonstrates strong positive linear correlation between average VLM task performance and ViCrt- scores. ViCrit- scores rise almost linearly with model's 's average performance across eight vision- language tasks (r = 0.96), showing that the benchmark effectively evaluates the visual perception, and is strong proxy for overall VLM capability. In the 7B class, performance rises step-wise from WAw----Viiion--/ Molmo-7B through I.LVV-OOne Vision-7B, Intern VI. 2.5-8B, and Qwen2.5-V1.-7B, to ViCrit-RI--7B
that tops every metric. The pattern repeats at the 72B scale where ViCrit-RI--2B achieves the best performance on both ViCrit-hench and general VLM evaluation.
This finding echoes original motivation in building ViCrit-bench, with the hypothesis that ViCrit accuracy could foreshadow a VLM's perception capability as well as the overall multimodal performance. Furthermore, the consistency of the ordering across scales suggests that ViCrit-RL's better hallucination performance in Finding 1 is not a by prooduct of merely training on hallucination detection. Rather, models that learn to "cross-check textual claims against visual evidence" via ViCrit also perceive and reason better on general VL problems, such as chart, math, and abstract images.
5.3 Qualitative Results
We showcase two representative cases (Figure ( that reveal how training on the ViCrit task sharpens visual perception and, consequently, improving VLM performance. Example 1. ViCrit-RL-72B is

---

ViCrt-Bench Performance
25
20
50
55
60
Average VLM Task Performance
Figure 5: Correlation between average VLM task performance and ViCrit- performance (Task Avg. and Overall columns in Table Each point represents different model, and the fitted linear regression line highlights a strong positive relationship, indicating that better ViCrit-Bench results are associated with higher stronger VLM capabilities.
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Figure 6: Two examples demonstrate the behavioral differences between models before and after training with the ViCrit task. It can be seen that ViCrit-RL- RL-72B pays closer attention image details and arrives at the correct final reasoning through its enhanced perception capabilities.
able to accurately identify all objects in the image in a clockwise order, capturing detailed attributes such as color and shape, and successfully making the correct calculation. Example 2. ViCrit-RL-72B correctly identifies all relevant visual Ittails-includee object-and and uses this information to derive the correct answer. contrast, Qwen2.5-VL. L---sttuet fails to capture the complete visual content due to its limited perception ability, leading to incorrect reasoning. These examples demonstrate that training on ViCrit task significantly improves visual perception, which is a crucial foundation for enhancing the VL.M performance.
colors and the number of edges of each
Conclusion
We have presented ViCrit, an RL proxy task that trains VLMs to pinpoint fine-grained synthetically injected visual hallucinations paragraph-lenghe captions. Because each targeted span is unambigu- ously verifiable, ViCrit provides a challenging yet noise-free reward signal that compels models to intermalize stronger perceptual strategies, yielding consistent gains across broad suite of benchmarks. Furthermore, we release -CCit, carefully curated dataset that enables rigorous evaluation of VLM perception. We hope this new task will spark further breakthroughs in multimodal RI., from standalone VLMs end-to-end fo-end-trained tool-augmented multimodal agents.
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Appendix
A Prompts used in experiments
A.1 Prompt for Training Data Generation
We provide the prompt used for generating ViCrit task training data in Table 6
A.2 Prompt for ViCrit-Bench Evaluation
We provide the prompt used for ViCrit- evaluation in Table 3
Table 3: Prompt template used for ViCrit-Bena evaluation.
Prompt Template:
You are provided with an image and the description corresponding to this image. There is one hallucination in this description. Find out the hallucination phase and answer with the hallucination phase directly in list. Your output should only be a list that contains the ballucination phase you find. Description:
B Training Hyperparameter
We provide the training hyperparameters in Table
Table 4: Training Hyperparameters
Name
Hyperparameter
Global batch size
64
Learning rate
1 x 10-6
Weight decay
x 10-2
KL coefficient
1 1x 10-2
Max response length
2048
Min response length
1024
Max pixels
802816
Rollout
Rollout batch size
512
Rollout temperature
1.0
Rollout top-p
0.99
GPU memory utilization
0.6
C Comparison with SFT
In this section, we perform SFT on Owen-2.55VLL-77 and 72B using 900k captioning samples from PixMo-Cap, and compare the results with ViCrit-RI. models trained using the same amount of data through ViCrit task RFT. As shown in Table 5 we find that although SFT significantly reduced hallucination in VLMs, it do not lead to notable performance improvements benchmarks-an fact, the 7B model even shows a performance drop. This highlights the effectiveness of ViCrit task RFT, which not only reduces hallucinations but also generalizes well to enhance VLM performance on general reasoning tasks.
general
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Table 5: Comparison between ViCrit-RL and ViCrit-RL with using same captioning data for SFT. We find that although hallucinations in the VLM significantly reduced after SFT, the performance improvement is difficult to generalize to general tasks.
Hallucination Benehmurk
General benchamrk
Vista Vista
Math Vision resemini
Math Vorse
CHAIRS
CHAIRI
MMMM
MMStr
MM. Vot
rousuming
MANHL
Model
Avg.
Owen2.5-VL-7B-BE 5-2----------
28.0
5.1
3.74
67.8
23.6
44.5
50.6
61.7
66.0
49.3
41.4
50.61
71. CapSFT
25.5
4.4
3.78
67.4
20.1
44.3
52.1
53.4
64.7
47.3
38.0
48.41
ViCrit-RL-7B
25.2
4.5
3.77
52.0
61.9
47.8
53.01
(Ours Qwen).5-7B)
-2.8
-0.6
+0.03
+2.9
+2.1
+1.8
+1.4
+0.2
+1.1
+3.3
+6.4
+2.40
26.4
Dwen2.5-VL-72B-CppSS
21.6
3.6
3.89
76.1
34.8
57.9
65.3
68.9
76.5
63.0
44.7
60.78
ViCHi-RRL-72B
21.0
3.9
3,91
77.3
40.1
59.8
66.0
69.8
77.1
65.8
49.4
63.16
(Ours Qwen2.5-72B)
-5.4
+2.5
+4,9
+1.4
+0.8
+3.38
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Table 6: Prompt used for training data generation.
You are a helpful assistant designed to manipulate text with precision. Your task is as follows:
1. Identify all noun phrases in a given paragraph. A noun phrase consists of a noun and its modifiers (e.g., "the wooden bridge." "a flock of birds"). Noun phrase is two to five words long. Do not output list of multiple noun phrases.
Randomly select one noun phrase from the list, it can be small background objects, scene text, foreground objects. Try to select scene text and small background objects more aften when possible. 3. Replace the chosen noun phrase with another phrase that is visually similar, such as changing the object attributes, replacing the object with visually similar noun, adding and removing characters within the scene text. The replacement should be visually similar but not identical to the original phrase. Be creative and don't always focus on the most obvious or common replacements such as color.
4. However, the replacement should introduce clear change, such that it is impossible to be ambiguous. The change should be directly related to image and be visual description. Do not only change words its synonyms or make ambigious changes. Do not merely change words to its synonyms. Do not merely change words to its synonyms. Do not merely change words to its synonyms.
Ensure the edited paragraph is still a plausible image description, and the change is not too obvious.
and changed phrase in <Af- terschangek<Aftter. <Caption> is used to give input caption and should not be generated. Perform this transformation accurately and naturally. Here are some examples:
6. Group the original phrase in <Before>orrginak</Befooo
1. <Caption>Thii This image appears to he a screenshot taken from an iPhone displaying the interface of a food delivery app, likely DoorDash, around the Chicago and Gary, Indiana area. The top of the screen indicates the time 2:30 PM, with the phone connected to an LTE network. The battery icon suggests a low battery level of 15-20
Central to the image is map highlighting various regions with color codes: red areas represent high traffic or demand, likely meaning those areas are "busy" for delivery drivers, as indicated by red text banner. Lighter red and green sections represent varying levels of demand.
At top of the image, a black banner labeled "Promos" displayed, accompanied by a blue notification bell icon with the number two beside it, indicating two notifications.
The bottom of the screen shows a black navigation bar. It contains options for "Dash," " "Schedule," " "Account," "Ratings." " and "Farnings." " The "Dash" option is highlighted in red, suggesting it is currently selected. Centrally located in this bar is red "Dash Now" button, implying that the user can hegin delivering immediately. An additional black banner. located just above the navigation bar, reads "In... Hammond."
Overall, this detailed caption gives comprehensive idea of the app's functionality, likely indicating arcas of high demand where food delivery services needed the most.</Captions <Before>a low battery level of 15-20%</Beforc>>
<After>a high battery level of 75-80%</AAter>
2. <Caaption>TTi The image depicts a screenshot from a strategy video game with a third-person, aerial view. The central character, named Anselm, navigates through complex, industrial-style building that evokes the aesthetic of games like Metal Gear. The environment dark and futuristic, with certain areas illuminated, revealing various paths and stairs. The top left corner displays the yellow text "Instructor Eastwood," alongside graph-like design. The upper center features game-related instructions in white text stating "Defensive Measures- Use Range to Your Advantage," with the notations "5" and "5,1" accompanying the instructions. Additionally, a map of the area is situated in the lower left corner, while the bottom right corner features interactive elements or key potentially indicating available weapons. The overall scene suggests a mission-focused gameplay scenario requiring strategic maneuvering and tactical decision-making -aaa--aaaannnaaaiion>>>>>.> </Caption>>
<Before>text "Instructor Bastwood,"<//effrrg
<After>text "Instructor <estw
<Caption>The image depicts dimee--aiiiiioaal panoramic view of a conference room where a business meeting is taking place. The setting is a typical meeting room with white walls, fluorescent lighting, and windows cquipped with blinds on some, including wooden slats on one, At the center of the room is a round, yellow table that appears slightly distorted duc to the panoramic effect. On this table, there are various items, including box of tissues, a white mug, teacup, and pamphlets.
Surrounding the table, seated in a circle, are eight individuals. They appear to mixed group of men and women, predominantly of Asian descent, and are dressed in a variety of attire ranging from business to casual. All attendees are wearing name tags on the left side of their chests, indicating their participation in the meeting. Their seating arrangement includes black chairs with green backs, and each person either has their hands folded their laps or is holding something, possibly a drink.
From left to right, the attendees include woman in a peach-colored t-shirt with writing, a man a blue shirt, a woman in a gray sweater, a woman in a green shirt, another woman in a blazer, an empty chair, man in yellowish shirt, man wearing ball cap, and man in blue and green jacket. One notable aspect is that the meeting environment, though professional, is quite understated with minimalistic decor and standard conference room fumishings2&(Cattions <Befforeseight individuals</Before>> <After>seven individualsc/After>
Here is the input caption: <Caption


Reviews:
[{"review_id": "ohwAanAye8", "reviewer": "Reviewer_Fe2P", "summary": "The paper introduces a novel vision-centric reinforcement learning task, ViCrit, which requires VLMs to identify subtle visual hallucinations within image captions. This task is designed for efficient and accurate evaluation and aligns well with reinforcement learning training frameworks. Building on this task, the paper further proposes ViCrit-Bench as a diagnostic benchmark to assess the fine-grained visual perception capabilities of vision-language models across four distinct visual domains.", "questions": "1. Could the authors provide details on the GRPO parameter settings used during training for the ViCrit task, as well as visualized curves of relevant metrics during the training process?\n\n2. The paper only compares model performance before and after training on the ViCrit task. Could the authors also present comparisons where the same model is trained on other  tasks or datasets, to better demonstrate the advantages of ViCrit?\n\n3. Have the authors conducted experiments on other base models to validate the adaptability and effectiveness of the ViCrit training across different model architectures?", "limitations": "the paper may have the following limitations:\n1.It lacks sufficient information related to the reinforcement learning training process.\n2.It does not provide performance comparisons of the model on other tasks or datasets, limiting the understanding of its generalizability.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength：\n1.The paper proposes a novel vision-centric reinforcement learning task, ViCrit, which effectively detects fine-grained visual hallucinations in image captions and offers strong practical value and evaluation efficiency.\n2.Building upon the ViCrit task, the authors develop ViCrit-Bench, a benchmark covering four visual domains that systematically diagnoses the visual perception ability of multimodal models.\n\nWeakness：\n1.The paper lacks detailed information on the GRPO parameter settings used in ViCrit training and does not provide visualized training metrics, which affects reproducibility and methodological transparency.\n2.The experiments only compare model performance before and after ViCrit training, without comparing against other tasks or datasets, making it difficult to fully demonstrate the unique advantages of the ViCrit task.", "was_revised": true, "final_justification": "Since this work is primarily focused on reinforcement learning tasks, I still have some concerns regarding the lack of detailed description of the reinforcement learning training process in the manuscript. However, considering that the authors have open-sourced the relevant resources, and after taking into account the comments from other reviewers, I am willing to raise my score to 4.", "rebuttal": "We thank Reviewer Fe2P for the thoughtful and constructive review. We are glad that you find our ViCrit task to be novel, practically valuable, and efficient to evaluate, and that you appreciate the diagnostic contribution of ViCrit-Bench across four visual domains.\nWe respond below to your concerns regarding reproducibility, comparisons, and generalizability:\n> W1: GRPO training details and visualization metrics\n\n- **All GRPO hyperparameters are now disclosed, and training reward values are reported to illustrate learning dynamics.** Please see the table of GRPO hyperparameters below, along with stepwise reward progression for both 7B and 72B models. Due to NeurIPS rebuttal policy, we cannot include figures, but we will include full training curves in the final version.\n\nHere are all the training hyperparameters used for GRPO training:\n|     name       |hyperparameter|\n| :----: |:----:|\n|global_batch_size|64 |\n|learining rate |1e-6 |\n|weight decay| 1e-2|\n|kl coef| 1e-2|\n|max response length| 2048|\n|min response length| 1024|\n|max pixels|802816 |\n|rollout n| 8|\n|rollout batch size| 512|\n|rollout temperature| 1.0|\n|rollout top_p| 0.99|\n|gpu memory utilization| 0.6|\n\n**ViCrit-RL-7B** training reward curve\n|     training step       |reward|\n| :----: |:----:|\n|1|0.14414 |\n|10|0.39873 |\n|20 |0.53892 |\n|30| 0.56462|\n|40| 0.57324|\n|50| 0.57542|\n|60| 0.59585|\n|70|0.60112 |\n|80| 0.62224|\n|90| 0.60659|\n|100|0.61064 |\n|200 | 0.63147|\n|300|0.64309 |\n|400|0.65432 |\n|500|0.67371 |\n|600|0.66951 |\n|700| 0.66497|\n|800| 0.68477|\n|900| 0.68008|\n|1000| 0.69271|\n\n**ViCrit-RL-72B**  training reward curve\n|     training step       |reward|\n| :----: |:----:|\n|1|0.30742 |\n|10|0.48672 |\n|20 |0.47383 |\n|30| 0.51836|\n|40| 0.54531|\n|50| 0.56055|\n|60| 0.59336|\n|70|0.59440 |\n|80| 0.59688|\n|90| 0.57578|\n|100|0.61797 |\n|200 | 0.62370|\n|300|0.61315 |\n|400|0.63906 |\n|500|0.67773 |\n|600|0.67513 |\n|700| 0.68711|\n|800| 0.67279|\n|900| 0.67996|\n|1000| 0.71055|\n\n\n- **We will open-source our training code and logs for full reproducibility.** These will be made available with our camera-ready version, including configuration files and scripts for ViCrit task construction and GRPO optimization.\n\n\n> W2: Lack of comparisons to other tasks or datasets\n\n- **ViCrit is not designed to replace existing tasks but rather complements them with a perception-verifiable RL objective.** Our goal is to show that ViCrit enhances fine-grained visual perception via reinforcement learning with a verifiable reward—an orthogonal axis to many existing multimodal tasks that optimize other cognitive capabilities.\n- **We include new comparisons against SOTA models trained on other tasks (Vision-R1[1], R1-OneVision[2], ThinkLite-VL[3]), showing ViCrit’s strong hallucination mitigation and comparable general reasoning.** The results are provided in the following table:ViCrit-RL-7B reduces hallucinations (CHAIRs/i) while retaining competitive accuracy on general tasks like MMVet, Blind, and Charxiv.\n\n|            |CHAIRs| CHAIRi | MM-Hal | MathVista| MathVision | MathVerse | MMMU    | MMStar | MM-Vet| Blind |Charxiv |\n| :----: |:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n|Vision-R1-7B |26.4 |5.2 |3.68 |71.5 |26.6 |51.9 |50.5 |60.2 |66.2 |38.0| 36.1 |\n|R1-Onevision-7B |27.8 |5.8 |3.65 |64.1 |29.9 |46.4 |49.2 |57.8 |66.2 |39.2| 40.9 |\n|ThinkLite-VL-7B| 25.8| 4.8| 3.79| 75.1| 32.9| 52.1| 55.5| 65.0| 67.8| 49.2| 45.8|\n|ViCrit-RL-7B| 25.2| 4.5| 3.77| 70.7| 25.7| 46.3| 52.0| 61.9| 67.1| 52.6| 47.8|\n|ThinkLite-VL-7B+ViCrit-RL| 24.8| 4.2| 3.80| 75.5| 32.2| 52.5| 56.7| 65.6| 67.6| 52.8| 48.4|\n\n\n- **To evaluate compositional benefits, we applied ViCrit-RL on top of ThinkLite-VL-7B and observed further improvements on both hallucination and reasoning benchmarks.** Notably, ThinkLite-VL-7B+ViCrit-RL sets **a new SOTA on MathVista (75.5)** among 7B-level VLMs. This suggests ViCrit training encourages better visual verification and reduces hallucination even when stacked atop other reasoning-optimized models.\n\n\n> W3: Experiments on other base models\n\n- **We validated ViCrit on InternVL-2.5-8B[4] to test generality, and observed consistent gains across all evaluated benchmarks.** As shown in our results, ViCrit-RL improved both hallucination mitigation and downstream reasoning performance compared to the base InternVL-2.5-8B.\nThis confirms that ViCrit is architecture-agnostic and generalizes well beyond our primary Qwen-2.5-VL base.\n\n|            |CHAIRs| CHAIRi | MM-Hal | MathVista| MathVision | MathVerse | MMMU    | MMStar | MM-Vet| Blind |Charxiv |\n| :----: |:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n|InternVL-2.5-8B |29.2| 5.4| 3.65| 64.4| 22.0| 39.5| 54.9| 62.8| 68.8| 47.6| 32.9 |\n|ViCrit-RL-InternVL-2.5-8B| 27.8| 4.6| 3.69| 66.8| 23.9| 41.7| 55.5| 63.7| 69.2| 49.2| 35.6|\n\n\nThank you again for your thoughtful review. We are excited to open-source ViCrit and ViCrit-Bench to the community and believe they can serve as foundational tools for future multimodal RL research. \n\n\nReference:\n\n[1] Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models. Huang et. al. arXiv:2503.06749\n\n[2] R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization. Yang et. al. arXiv:2503.10615\n\n[3] SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement. Wang et. al. arXiv:2504.07934\n\n[4] Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling. Chen et. al. arXiv:2412.05271"}, {"review_id": "95P54NNe4u", "reviewer": "Reviewer_qk6D", "summary": "This paper proposes a novel perception task, called ViCrit, which requires VLMs to predict challenging corrupted words in the original detailed captions. This task is proven to be effective for training VLMs with RL methods. Extensive evaluations on both general multimodal benchmarks and hallucination examination benchmarks demonstrate the effectiveness of the proposed method. The authors also introduce a benchmark, ViCrit-Bench, to validate the reconstruction accuracy of corrupted words in captions.", "questions": "In the section 4.1, the images are categorized into four domains. I think for vision understanding the GUI images are also important and common for VLMs. Why this domain was not considered in your data preparation?", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n- This paper proposes ViCrit, a novel perception task that prompts models to predict corrupted words in original captions. This approach is simple to implement and useful for training VLMs with RL strategies.\n- The authors have conducted extensive evaluations on both general and hallucination benchmarks, demonstrating the effectiveness of the proposed method.\n -  A new benchmark, ViCrit-Bench, has also been introduced to evaluate the reconstruction accuracy of corrupted words in detailed captions.\n\nWeakness\n - Limited RL training details. Since unstable training is a common issue in RL, key details (e.g., rollout number, reward curve) are crucial for the research community, especially in the VLM domain.\n - No training recipe is provided, making reproducibility challenging.\n - For ViCrit-Bench, human performance metrics are missing. These would help estimate the benchmark’s approximate upper bound.\n - Lack of benchmark examples or case studies. Without concrete cases, it is difficult to assess the benchmark’s difficulty or practical validity.", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer qk6D for the thoughtful and constructive feedback. We appreciate that you find our proposed ViCrit task to be novel and useful, and that you recognize the value of our extensive evaluations and the introduction of ViCrit-Bench. Below, we address all concerns and questions raised:\n\n> W1: Limited RL training details\n\n- **We now provide full GRPO hyperparameters and reward values across training steps to support reproducibility and transparency.** The table below lists all GRPO hyperparameters used in our ViCrit-RL training:\n\n|     name       |hyperparameter|\n| :----: |:----:|\n|global_batch_size|64 |\n|learining rate |1e-6 |\n|weight decay| 1e-2|\n|kl coef| 1e-2|\n|max response length| 2048|\n|min response length| 1024|\n|max_pixels|802816 |\n|rollout n| 8|\n|rollout batch size| 512|\n|rollout temperature| 1.0|\n|rollout top_p| 0.99|\n|gpu memory utilization| 0.6|\n\n- **We report reward progression during training as a substitute for full reward curves.** Due to rebuttal policy, we cannot include figures or links. Below are reward values at various training steps, we will include the complete training reward curve in the final version of the paper to better illustrate the training dynamics:\n\n**ViCrit-RL-7B**\n|     training step       |reward|\n| :----: |:----:|\n|1|0.14414 |\n|10|0.39873 |\n|20 |0.53892 |\n|30| 0.56462|\n|40| 0.57324|\n|50| 0.57542|\n|60| 0.59585|\n|70|0.60112 |\n|80| 0.62224|\n|90| 0.60659|\n|100|0.61064 |\n|200 | 0.63147|\n|300|0.64309 |\n|400|0.65432 |\n|500|0.67371 |\n|600|0.66951 |\n|700| 0.66497|\n|800| 0.68477|\n|900| 0.68008|\n|1000| 0.69271|\n\n**ViCrit-RL-72B**\n|     training step       |reward|\n| :----: |:----:|\n|1|0.30742 |\n|10|0.48672 |\n|20 |0.47383 |\n|30| 0.51836|\n|40| 0.54531|\n|50| 0.56055|\n|60| 0.59336|\n|70|0.59440 |\n|80| 0.59688|\n|90| 0.57578|\n|100|0.61797 |\n|200 | 0.62370|\n|300|0.61315 |\n|400|0.63906 |\n|500|0.67773 |\n|600|0.67513 |\n|700| 0.68711|\n|800| 0.67279|\n|900| 0.67996|\n|1000| 0.71055|\n\n> W2: Missing training recipe\n\n- **We used EasyR1 as our training codebase and will release recipes for reproducibility.** We use Qwen-2.5-VL-7B/72B as base models and train on ViCrit directly via GRPO, with **no supervised fine-tuning or knowledge distillation.** Reward formulation is detailed in **Section 3.2, lines 159–165**, and we will include the complete training recipe in the final version and code release.\n\n> W3: Missing human performance on ViCrit-Bench\n\n- **We agree that human accuracy is a crucial baseline, and we have now begun collecting expert annotations to report this in the camera-ready version.** Establishing a human upper bound will help contextualize model performance, especially given the fine-grained nature of ViCrit tasks (e.g., subtle hallucinations in shape, count, or text). We are employing annotators familiar with vision-language tasks and providing the full paragraph-length captions and images as shown in ViCrit-Bench, under the same evaluation setting used for models.\n- We thank the reviewer for highlighting this point—we believe it will strengthen the benchmark’s utility and interpretability for future work.\n\n\n> W4: Lack of benchmark examples or case studies\n\n\n- **Figure 3 provides examples from each hallucination category; we will expand with full case studies in the appendix.** Due to page limits, we only included hallucination spans, not full captions. The revised version will add full examples for each hallucination type to better convey task difficulty and practical use.\n\n\n> Q1: GUI images not considered in domain categorization\n\n- **GUI images are included and categorized under the “document” domain.** As shown in the second example of Figure 3, our benchmark includes GUI-like screenshots (e.g., software interfaces, forms). We clarify this classification in Section 4.1, lines 175–181, and will make this more explicit in the revised text.\n\n\nThank you again for your helpful suggestions. We are committed to improving reproducibility and clarity in the final version and believe ViCrit and ViCrit-Bench will provide valuable tools for advancing VLM research."}, {"review_id": "anUqwQyPYu", "reviewer": "Reviewer_DvR5", "summary": "This paper introduces a novel task called the ViCrit task, which enables models to detect visual hallucinations in captions and locate the corresponding errors. The authors also construct a challenging benchmark, ViCrit-Bench, for hallucination detection, which includes four types of visual perception. Using this task and dataset, they employ RL to train the inference model ViCrit, improving the model's performance in hallucination mitigation, mathematics, VQA, and counting tasks.", "questions": "1). Could you provide a detailed data comparison analysis and compare your dataset with standard VQA datasets to demonstrate its advantages?\n\n2). Could you provide comparisons with other reasoning models, such as Vision-R1, R1-Onevision, etc., to demonstrate the performance advantages of your method?\n\n3). Could you provide more detailed experiments and analysis to explain why training on visual perception tasks improves performance and generalization in reasoning tasks such as mathematics?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1). The proposed new task is interesting, and the construction of the dataset is reasonable. The ViCrit model, trained using RL, shows improvements across various tasks.\n\n2). The paper is well-written, with a clear format and easy readability.\n\nWeaknesses:\n\n1). The paper does not clearly explain the strong motivation for introducing this dataset. Common VQA datasets are also verifiable, and it is unclear what advantages this dataset offers over existing ones.\n\n2). The experimental comparisons are unfair, as there is no comparison with other reasoning models such as Vision-R1, R1-Onevision, etc.\n\n3). The ViCrit task primarily focuses on visual perception. Further experimental analysis is needed to demonstrate how training on this task can enhance the model's generalization ability in reasoning tasks, such as mathematics.", "was_revised": true, "final_justification": "Thank you for the authors' comprehensive response, which has fully resolved all my concerns. Given the method's contribution to reducing hallucinations induced by RL training, I am inclined to recommend acceptance of the paper. I suggest that the authors incorporate these additional experiments and discussions into their revised manuscript to further strengthen the submission.", "rebuttal": "We thank Reviewer DvR5 for the detailed and thoughtful review. We are encouraged that you find our ViCrit task interesting, our dataset construction reasonable, and our paper clear and well-written. Below we address your questions and concerns:\n\n> W1&Q1: What is the advantage of ViCrit over standard VQA datasets?\n\n- **ViCrit is designed specifically for reinforcement learning with verifiable perception tasks—something standard VQA datasets cannot support.** While VQA datasets provide short question-answer pairs, they rarely require exhaustive scene grounding. In contrast, ViCrit uses **long-form captions with one injected hallucination**, requiring the model to **check all visual details and identify the corrupted span**. This makes the task more challenging and better aligned with scalable RL training, where exact match rewards are necessary.\n- **Standard VQA datasets often admit multiple plausible answers or rely on language priors.** For example, \"What is the man doing?\" may be answered \"playing tennis\" or \"hitting a ball\"—both acceptable, but hard to score in RL. ViCrit avoids this ambiguity by offering binary string-matching rewards at the span level, enabling efficient and noise-free optimization.\n- **We will include a comparison of task properties (e.g., ambiguity, span length, reward verifiability, image domain diversity) with standard VQA datasets in the final version.**\n\n> W2&Q2: Why are there no comparisons with reasoning models like Vision-R1 or OneVision?\n\n- **We now include experimental comparisons against Vision-R1-7B[1], R1-OneVision-7B[2], and ThinkLite-VL-7B[3].** All models use Qwen2.5-VL-7B as the base. The table below compares ViCrit-RL-7B with these strong reasoning-focused models:\n\n|            |CHAIRs| CHAIRi | MM-Hal | MathVista| MathVision | MathVerse | MMMU    | MMStar | MM-Vet| Blind |Charxiv |\n| :----: |:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n|Vision-R1-7B |26.4 |5.2 |3.68 |71.5 |26.6 |51.9 |50.5 |60.2 |66.2 |38.0| 36.1 |\n|R1-Onevision-7B |27.8 |5.8 |3.65 |64.1 |29.9 |46.4 |49.2 |57.8 |66.2 |39.2| 40.9 |\n|ThinkLite-VL-7B| 25.8| 4.8| 3.79| 75.1| 32.9| 52.1| 55.5| 65.0| 67.8| 49.2| 45.8|\n|ViCrit-RL-7B| 25.2| 4.5| 3.77| 70.7| 25.7| 46.3| 52.0| 61.9| 67.1| 52.6| 47.8|\n|ThinkLite-VL-7B+ViCrit-RL| 24.8| 4.2| 3.80| 75.5| 32.2| 52.5| 56.7| 65.6| 67.6| 52.8| 48.4|\n\n- **ViCrit-RL-7B achieves the best hallucination mitigation (CHAIRs/i, MMHal), and competitive results on general reasoning benchmarks.** It outperforms Vision-R1 and R1-OneVision on all fIVE visual-centric reasoning benchmarks (MMMU, MMStar, MM-Vet, Blind, CharXiv), and matches or exceeds them on MathVista, MathVision, and MathVerse.\n- **Furthermore, we apply ViCrit-RL on top of ThinkLite-VL-7B, yielding improved performance across the board.** This joint model achieves a new SOTA of 75.5 on MathVista among 7B-level VLMs, demonstrating ViCrit's effectiveness and compatibility with existing reasoning-focused methods.\n\n\n> W3&Q3: Why does training on a perception task improve reasoning performance?\n\n- **ViCrit improves reasoning by improving visual grounding—especially for math and abstract image tasks.** Many reasoning benchmarks (e.g., MathVista, CharXiv, VLMsAreBlind) require the model to perceive layout, count objects, read text, and track spatial relations before reasoning. ViCrit forces models to internalize how to perceive accurately, which in turn supports better reasoning outcomes.\n\n- **We illustrate this in Figure 5 of the paper, where ViCrit-RL-72B generates more accurate and complete intermediate thoughts than the base model.** These qualitative results show ViCrit-trained models pay more attention to visual details (e.g., color, size, shape, text), which enables more robust chain-of-thought reasoning.\n- **We will include additional case studies in the appendix to further show how perception improvements propagate to reasoning quality.**\n\n\nWe thank the reviewer again for raising these important points. We believe ViCrit fills a critical gap by offering the first vision-centric, verifiable RL task for VLMs, and we are excited to open-source the code and benchmark to support broader adoption and future research.\n\nReference:\n\n[1] Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models. Huang et. al. arXiv:2503.06749\n\n[2] R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization. Yang et. al. arXiv:2503.10615\n\n[3] SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement. Wang et. al. arXiv:2504.07934"}, {"review_id": "DnnqQvXRQJ", "reviewer": "Reviewer_Nwtd", "summary": "ViCrit is a proxy task to enhance visual perception of Vision-Language Models (VLMs). The objective is to teach a VLM to spot and correct a visual hallucination that was synthetically injected in an existing image caption by prompting a proprietary VLM such as GPT-4. The correctness of the generated caption is easily verifiable by exact matching, and this allows for optimization of the target VLM with GRPO, using an additional format reward to incentivize thinking. The authors also propose a new benchmark called ViCrit-Bench, where they show that Qwen2.5-VL-Instruct (7B and 72B) achieve state-of-the-art performance after training with the ViCrit task.", "questions": "I think that this paper is valuable, and I encourage the authors to address the weaknesses listed above, in particular **W1**.\n\nMoreover:\n\n- Did the authors try to build ViCrit annotations starting from shorter captions (e.g. 100 words rather than 200)? Has this even had an impact on the final performance?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**STRENGTHS:**\n1. ViCrit introduces an intuitive objective that can act as the reward signal for reinforcement learning. The procedure to build ViCrit data is simple (although it may require proprietary VLM to work the best) and should be scalable given that image captioning data is relatively simple to obtain compared to other kinds of annotations, *e.g.* bounding boxes, segmentation masks, etc. The other contribution of this work, ViCrit-Bench, is well-documented and appears to be a challenging benchmark even for proprietary VLMs.\n\n2. The effectiveness of the ViCrit task is exemplified by the strong performance achieved by Qwen2.5-VL after reinforcement learning. Notably, ViCrit enhances performance across different benchmarks other than ViCrit-Bench, such as visual hallucination and mathematical reasoning.\n\n**WEAKNESSES:**\n1. Experiments are carried out exclusively on Qwen2.5-VL. Despite its solid results, we urge to apply ViCrit on different VLMs (for instance, LLaVA-1.5 [1]) to test whether it can generalize to other models.\n\n2. *(minor)* Table 2 shows encouraging results overall. However, ViCrit severely hurts Qwen2.5-VL performance on \"Text\" hallucinations. The authors should discuss this point and provide insights on the possible causes.\n\n3. *(minor)* Appendix E readily compares ViCrit against an SFT baseline, given the same amount of data. I appreciate it, and yet I would have included additional experiments to further isolate the role of GRPO and the incentive to reason: what if we simply fine-tune a VLM with SFT to generate the non-hallucinated caption, given the hallucinated caption as prompt? This ties up ViCrit and SFT also on the kind of data being used, and not only on the amount.\n\n**References**\n\n[1] Liu, Haotian, et al. \"Improved baselines with visual instruction tuning.\" CVPR 2024.", "was_revised": true, "final_justification": "My main concern was on the generalization of ViCrit beyond Qwen 2.5, which the authors have properly addressed during the rebuttal. The paper is technically robust, and the introduction of ViCrit-Bench is a further contribution of this work. I raise my score to Accept.", "rebuttal": "We thank Reviewer Nwtd for the encouraging and insightful review. We are pleased that you find the ViCrit task intuitive, the benchmark valuable, and our results strong. Below we address your key concerns and suggestions in detail:\n>W1: Generalization beyond Qwen2.5-VL\n\n- **We confirm that ViCrit generalizes to other model architectures and report results on InternVL-2.5-8B[1].** To assess generalizability, we applied ViCrit training to InternVL-2.5-8B without any architecture-specific tuning. The resulting model, ViCrit-RL-InternVL-2.5-8B, shows consistent improvements across all evaluated benchmarks:\n\n|            |CHAIRs| CHAIRi | MM-Hal | MathVista| MathVision | MathVerse | MMMU    | MMStar | MM-Vet| Blind |Charxiv |\n| :----: |:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n|InternVL-2.5-8B |29.2| 5.4| 3.65| 64.4| 22.0| 39.5| 54.9| 62.8| 68.8| 47.6| 32.9 |\n|ViCrit-RL-InternVL-2.5-8B| 27.8| 4.6| 3.69| 66.8| 23.9| 41.7| 55.5| 63.7| 69.2| 49.2| 35.6|\n\n- **These results support that ViCrit can transfer across model architectures and training pipelines.** We agree that further applications (e.g., to LLaVA-1.5) are valuable, and we plan to explore this in future releases.\n\n>W2: Drop in “Text” hallucination performance\n\n- **The observed drop in “Text” hallucination accuracy stems from training data imbalance rather than a limitation of the ViCrit objective.** As detailed in Section 4.1, our PixMo-Cap-derived training set is dominated by natural images (~59%), with scene-text-heavy and document images comprising only ~24% and ~10% respectively. Since “Text” hallucinations are disproportionately concentrated in these underrepresented categories, models have limited exposure to them during RL fine-tuning.\n- **Importantly, ViCrit-RL still outperforms the base model across 10 of 11 benchmarks, including strong gains on abstract and document-heavy tasks (e.g., +3.3 on VLMsAreBlind, +6.4 on CharXiv).** This suggests that while one hallucination type underperforms, the overall visual perception is significantly improved.\n- **We have begun exploring class- and domain-aware data resampling and weighted reward shaping to mitigate this issue in ongoing experiments.** Additionally, we are expanding the ViCrit training set with more scene-text and document-rich samples (e.g., charts, scanned documents, GUI mockups) to support stronger generalization across all hallucination types.\n- **We view this as an opportunity to develop domain-adaptive RL pipelines for VLMs,** where model sensitivity to hallucination types can be tuned based on application needs (e.g., OCR-critical tasks vs. object-centric ones).\n\n> W3: Further SFT baseline for isolating the role of GRPO\n\n- **We followed your suggestion and added a new SFT baseline that generates the non-hallucinated caption given the hallucinated one.** We refer to this model as Qwen2.5-VL-7B-CapSFT-Generation, and report its performance below:\n\n|            |CHAIRs| CHAIRi | MM-Hal | MathVista| MathVision | MathVerse | MMMU    | MMStar | MM-Vet| Blind |Charxiv |\n| :----: |:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n|Qwen2.5-VL-7B-Instruct |28.0 |5.1 |3.74 |67.8 |23.6 |44.5 |50.6 |61.7 |66.0 |49.3| 41.4 |\n|Qwen2.5-VL-7B-CapSFT |25.5 |4.4 |3.78| 67.4| 20.1| 44.3| 52.1| 53.4| 64.7| 47.3| 38.0| 48.41|\n|Qwen2.5-VL-7B-CapSFT-Generation |25.9 |4.6 |3.77| 67.0| 19.8| 42.1| 51.4| 54.9| 64.3| 46.5| 39.1|\n|ViCrit-RL-7B| 25.2| 4.5| 3.77| 70.7| 25.7| 46.3| 52.0| 61.9| 67.1| 52.6| 47.8|\n\n- **This baseline confirms that ViCrit-RL outperforms SFT-based alternatives. These results isolate the value of reinforcement learning and verifiable perception-based rewards as central to ViCrit’s generalization capabilities.**\n\n> Q1: Impact of caption length\n\n- **We appreciate the reviewer’s insightful question—caption length plays a crucial role in ViCrit’s design and difficulty.** ViCrit is specifically constructed to promote fine-grained, holistic visual perception by embedding a single subtle hallucination into long, paragraph-length captions (~200+ words). These long contexts force the model to verify a wide range of objects, relations, and attributes before locating the anomaly—closely mimicking real-world multimodal grounding tasks.\n- **Shorter captions may reduce the perceptual search space and weaken the RL signal, as hallucinations become more easily detectable and less dependent on visual grounding.** This risks rewarding shallow pattern-matching strategies, rather than encouraging true image-text alignment.\n- Although our current dataset (PixMo-Cap) does not include a sufficient volume of shorter captions for a clean ablation, **we plan to conduct a systematic study** by synthetically truncating captions or controlling their verbosity. Such an analysis would quantify the tradeoff between perceptual load and learning benefit, and help determine whether longer contexts better support chain-of-thought-style visual reasoning.\n- **We believe caption length could also be a lever for curriculum-based RL**, where models start with shorter, easier examples and progress to dense, ambiguous scenes—an exciting future direction we plan to explore.\n\n\nWe appreciate your suggestions, especially regarding baselines and performance analysis. We will incorporate these findings and discussions in the revised version to further clarify the strengths and limitations of ViCrit.\n\n\nReference:\n\n[1] Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling. Chen et. al. arXiv:2412.05271"}]

Author rebuttal:

Meta-review: The paper introduces a new vision-centric reinforcement learning task, ViCrit, designed to enhance the fine-grained visual perception capabilities of Vision-Language Models (VLMs). The task involves detecting and correcting subtle visual hallucinations or corrupted words within image captions, with correctness being verifiable through exact matching. This makes the task well-aligned with reinforcement learning training frameworks such as GRPO. At the beginning, some reviewers thought this paper has certain issues, while after rebuttal and discussion stages, all reviewers think that the major concerns are adressed and all of them agree to accept this paper. Thus I also recommend to accept it.
Decision: Accept (poster)