More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models
Authors: Xinyu Tian
Shu Zou
Zhaoyuan Yang
Mengqi He
Fabian Waschkowski
Lukas Wesemann
Peter Henry Tu
Jing Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language Models
Multimodal Reasoning
Reinforcement Learning
Primary area: foundation or frontier models, including LLMs

Abstract: Reasoning has emerged as a pivotal capability in Large Language Models (LLMs). Through Reinforcement Learning (RL), typically Group Relative Policy Optimization (GRPO), these models are able to solve complex tasks such as mathematics and code generation. Building on these advances, recent research has sought to extend reasoning to Vision-Language Models (VLMs), yielding promising results across diverse visual tasks. Despite this progress, our study uncovers the dual nature of multimodal reasoning: while it substantially enhances logical inference and facilitates performance on challenging problems, longer reasoning length may gradually impair perceptual grounding, leading to recognition failures on otherwise basic visual questions. Through further analysis, we attribute this phenomenon to visual forgetting, wherein prolonged reasoning ength causes models to disregard visual input. To address this, we propose Vision-Anchored Policy Optimization (VAPO), a simple yet effective method that explicitly steers the reasoning process toward visually grounded trajectories. Our result model, VAPO-Thinker-7B, significantly strengthens the model's reliance on visual information and achieves new state-of-the-art results on various benchmarks.

Published as a conference paper at ICLR 2026
MORE THOUGHT, LESS ACCURACY? ON THE DUAL NA- TURE OF REASONING IN VISION-LANGUAGE MODELS
Xinyu Tian', Shu Zou Zhaoyuan Yang", Mengqi He², Fabian Waschkowski**
Lukas Wesemann* Peter Tu", Jing Zhang', "Australian National University *University of Melbourne
"GE Research
Maannodee
Firstnanc lastname edu. firstname las t nameege com
ABSTRACT
Reasoning has emerged as pivotal capability in Large Language Models (LLMss.. Through Reinforcement Learning (RL), typically Group Relative Policy Opti- mization (GRPO), these models are able to solve complex tasks such as mathemat- ics and code generation. Building on these advances, recent research has sought extend reasoning Vision-Language Models (VLMs), yielding promising re- sults across diverse visual tasks. Despite this progress, our study uncovers the dual nature of multimodal reasoning: while it substantially enhances logical infer- ence and facilitates performance on challenging problems, longer reasoning length may gradually impair perceptual grounding. leading to recognition failures on oth- erwise basic visual questions. Through further analysis, we attribute this phe- nomenon to visual forgetting, wherein prolonged reasoning length causes models to disregard visual input. To address this, we propose VISION-ANCHORE ICY OPTIMIZATION (VAPO), a simple yet effective method that explicitly steers the reasoning process toward visually grounded trajectories. Our result model, VAPO- VAPO-Thinker Thinker-7ea significantly strengthens the model's reliance on visual in- formation and achieves new state-of- off-th--art results various benchmarks.
POL-
INTRODUCTION
Reasoning has long been recognized as essential capability of Large Language Models (LLMs). Early approaches, such as chain-of-thought (Wei et al. 2022; Kojima al. 2022), encourage mod- els to produce step-by-step explanations before arriving at an answer, whereas more recent advances have introduced aha moments (Guo et al. 2025 Muennighoff et al. 2025), characterized by cogni- tive behaviors such self-reflection and verification. These developments have enabled models to address increasingly complex problems, e.g., mathematics and coding. Consequently, the commu- nity has recently sought integrate reasoning into Dision-Language Models (VLMs). By leverag- ing Reinforcement Learning (RL) such as Group Relative Policy Optimization (GRPO) (Shao et al.) 2024), researchers have trained models think while tackling challenging multimodal tasks, e.g., geometry or navigation (Yang et al. 2025c Huang et al.) 2025). Remarkably, this integration has also demonstrated promising results on traditional visual tasks, e.g., classification and detection (Liu et al. 2025 Shen et al. 2025), while yielding stronger generalization (Chu et al. 2025).
Building on these successful practices, reasoning has emerged powerful and seemingly uni- versal strategy for addressing a wide range of visual tasks. However, its limitations remain largely unexplored. Early doubts are raised by Li et al. (2025). which find that in certain scenarios. models trained with explicit reasoning result in only marginal gains compared to direct answering. More re- cently, Xia et al. (2025) observe a counter-intuitive trend: unlike in text domains, RI. multimodal settings often exhibits collapse. As accuracy improves during training, the reasoning process grad- ually shortens, suggesting that models tend to favor brief, less structured responses. These findings lead us ask: Is reasoning truly consistent performance booster for vision-language models?
Motivated by this question, we perform a zoom-in analysis of how the reasoning process impacts ac- curacy of existing VLMs. Beyond the standard settings of direct answering and full-step reasoning, we introduce an early decision mode, where the model is prompted to halt reasoning at selected log- ical breakpoints and produce immediate answer. This setup allows measure the contribution

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Published as a conference paper at ICLR 2026
LogicVista (Chart)
Early Decision
D: What is the total profit of top three industrios America?
thinne We are given chari from Reyes Heslop. Let's identify the profits of each industry:
Answer:
Leisure:44 4. (Pacific Rim). 7.4 (American) and 3.5 (European).
Reyes Heslop Consutting Profits (E millions
Time up. should stop thinking and give answer based on what have.s/thinke TanswerThhe sum of the profit is 20.5 millions.eenswer ranswer>
Ameiican
Eurspeen
Full Reasoning
4.5
thinke We are given a charl from Reyes Heslop. Let's idenlify the profits of each industry: Leisure: 4.5 (Pacific Rim), 7.4 (American) and 3.5 (European).
774
7P
58
9.9
Manufacturing: 6.3 (Pacific Rim), 7.2 (American) 4.0 (European).
3.
4. Uniliies: 6.2 (Pacific Rim), 811 (American) and 3.5 (European).
3.5
4.0
4.4
455
industries are Leisure (7.4), Manufecturing (7.2), Unilties (8. D. Let's sum the profit up: 7.4 7.2 + 8.1 22.7(millions))/hhhaa)
question, the top
Lokaure
Marafactutig
Retall
dovernmett
Utiiiss
<answer-The The total profit is 22.7 miliioos.//anme s/answer>
MMStar (Counting)
Early Decision
Q: In this given image, can determine the number of cats that appear? Answer: DT Answer:
<thinke Okay, are asked how many cats are there the photo. Let's have look. First, there an
cat lying down distinct 2. On the far loft, a kitten lias on its back with tiny paws stratched upwards.
yellow eyes.
Time up. I should stop thinking and give an answer based on what I have. hlhiik// photo.r/answer-
There are
Full Reasoning
<think> Okay, we are asked many cats there photo. Let's look.
First, there is an adult white cat lying down with distinct blue and yellow eyes.
2. On the far left, a kitten lies on its back with tiny paws stretched upwards.
There
cat
middle-boltom
photo, with
4. the right, closest to the mather's head, kitten is resting with belly showing.
5. On the far right, there is kitten hidden beneath, with only its black eyes visibla. <enswer-The number of cats 5.1/anseer
Figure 1: The examples where more reasoning yields less accuracy. We select Vision-R1 (Huang at 2025) as a representitive multimodal reasoning model and evaluate on established benchmarks. Given the ground truth (GT) answer, we examine three settings, i.e, direct (DT) answering, full reasoning, and early decision. Correct and incorrect responses are highlighted in green and red, respectively, while designed prompts for early decision are indicated in blue.
of each reasoning segment to the final outcome. Interestingly, our finding reveals that while early- stage reasoning may offer substantial gains, its advantages tend to saturate and may even reverse in later steps. To understand this phenomenon, we conduct an error analysis of the reasoning content in failure cases and find that perception errors, where the model incorrectly interprets visual details in the image, constitute the dominant category, accounting for over 50%. Moreover, the majority of these errors can be rectified with shorter reasoning via early decision, indicating the model is initially capable of producing correct answers but is ultimately misled by extended reasoning. For instance, as shown in Fig. [ the early stage of reasoning paths, i.e., early decision, enables the model to arrive at correct answers, whereas prolonged thoughts, i.e., full reasoning, often lead errors such as chart misreading or hallucination. This indicates the dual nature of reasoning: while it strength- ens logical inference and facilitates complex rroleem-ollving. longer reasoning length may weaken perceptual grounding, leading to susceptibility on otherwise straightforward visual questions.
This decline in perceptual capability constitutes a major bottleneck for multimodal reasoning. To further investigate its underlying causes, examine how attention to visual tokens evolves through- out the reasoning process. Our analysis reveals a marked decrease in visual attention as reasoning progresses, suggesting the emergence of reasoning may inadvertently reduce the model's reliance on visual input, a phenomenon we refer to as visual forgetting. To validate this hypothesis, we pro- pose two interventions: 1) visual replay, which reintroduces the input image to models at regular intervals during reasoning; 2) focus prompt, which prompts models to attend to the visual input at selected steps. Both methods are proven to alleviate performance degradation induced by reasoning, providing evidence that visual forgetting is a central factor constraining reasoning potentials.
While the two aforementioned remedies can partially alleviate visual forgetting, they incur substan- tial computational overhead during inference and fail to rectify the underlying behavioral deficien- cies of existing reasoning models. To address this, we propose VISION- RESION-ANCHEE ANCHORED POLICY OP- TIMIZATION (VAPO), a simple yet effective policy gradient algorithm that explicitly guides the rea- soning process toward a visually grounded trajectory. The key idea is to embed a sequence of visual anchors throughout the reasoning path. At each anchor, the model's perceptual capability is assessed by evaluating its responses to a set of primitive visual claims. Beyond standard outcome-base wards such as accuracy and format, we introduce perception reward, which quantifies the model's s overall perceptual grounding during reasoning by aggregating scores across all anchor points. Ex- perimental results demonstrate that our result model, VAPO-Th i nker-78: significantly enhances
re-
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the model's reliance visual imput during reasoning and achieves average gains of 2 ~ 4% over strong baselines on established benchmarks, setting a new state of the art.
In summary, contributions are as follows:
Despite the promise of multimodal reasoning, we identify pronounced dual nature, where reasoning enhances logical inference capability at the expense of perceptual accuracy as the length grows, constituting a major bottleneck to the overall effectiveness.
We further examine this side effect of reasoning and empirically demonstrate that it stems from visual forgetting, where prolonged reasoning length gradually reduces the model's reliance on visual input, leading to a substantial increase in perceptual failures.
We propose VAPO, a straightffrrward yet effective policy gradient algorithm designed to strengthen the model's dependence on visual information during reasoning, and demon- strate its effectiveness through experimental validation.
RELATED WORK
Reasoning in Large Language Models. Conventional research has posited trade-off between interpretability and accuracy (Wang et al. 2020 Koh et al. reasoning, however, has emerged that prompting LLMs (Wei et al. 2022
2020; Yao ct al. 2023b). The advent of
notable exception to the paradigm. Early works demonstrate
2025a Tian et al., (024) or applying Supervised Fine- Tuning (SFT) Yang et al. 2025a Cai et al.
Yao et al. 2023a Zou et al.
2024) to encourage step-by-
step explanations prior to answering could lead to substantial gains. These findings underscore the
critical role of constructing large-scale, high-quality chain-of-thought trajectories. More recently, the emergence of RL-based methods such as GRPO (Shao et al. 2024) has diminished the reliance on manually crafted reasoning paths. With only a limited number of examples, models are now capable of autonomously discovering optimal reasoning strategies and even exhibiting advanced behaviors such as self-reflection and verification (Yu et al. 2025 Zheng et al.) 2025). Consequently, Test-Time Scaling (TTS) (Muennighoff et al.. 2025 Snell al., [025 Qu et al. [225 Yao al.)
2025) has become a standard practice in LLMs, driven by the belief that longer or more elaborate reasoning consistently yields better performance. However, our empirical analysis challenges the universality of this assumption, revealing that TTS does not necessarily generalize to multimodal settings, particularly regarding its implications for VLMs.
Reasoning in Vision-Language Models. Building on the success of reasoning in LLMs a grow- ing line of research has sought to extend this capability to VLMs (Wang et al., 2025b Meng al. 2025 Wang et al. 2025a]] He et al. 20255] Tian et al. 2025b a
Zou et al. 2025b). Two predom- inant approaches have emerged: one leverages limited dugh-quaiity trajectories for cold-start via SFT. followed by RL (Peng et al. 2025 Yang et al.) 2025b Tan et al. 2025 Feng et al., 2025), while the other bypasses SFT entirely, employing RL alone to discover optimal reasoning paths (Liu et al.) 2025; Wang et al.] 2025d). Both approaches have yielded promising results various vi- sual tasks, particularly those requiring complex logical inference. However, recent findings indicate that, in certain scenarios, models trained with explicit reasoning offer little or no gains over di- rect answering (Li et al.] (002)). Moreover, training dynamics reveal that as accuracy increases, the length of generated reasoning tends diminish, implying a preference for shorter and more con- cise responses (Xia et al. 2025). These preliminary findings indicate that reasoning may not be a free lunch in the development of VLMs. To the best of our knowledge, our work presents the first systematic investigation into the double-edged nature of reasoning in VLMs, identifying visual forgetting as a key limitation and proposing effective solutions to mitigate this issue.
F Forgetting in Vision-Language Models. In fact, visual forgetting has long been a well-known yet insufficiently addressed issue. Since the emergence of VL.Ms, researchers have identified a pro- nounced text bias in these JLMM-bckbooned models, where responses are predominantly guided by textual input while visual cues are largely ignored (Chen et al. 2024a Fu et al., 2024). At the time, this modality imbalance is less apparent, as early VLMs typically generate short outputs and lack so- phisticated reasoning capabilities. Consequently, most prior efforts focus on test-time remedies such as contrastive decoding (Leng et al. 2024 Wang et al., (024c) or attention reallocation (Tu et al.) 2025 Gong et al.] 2024) to circumvent visual forgetting. However, with the recent rapid progress in multimodal reasoning, VLMs have increasingly shifted from generating short answers produc-
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8.41%
60
Recovery
Accurccy 24
Porception e
Perception (55.2556)
(38,6%%
Knowledge 71-
Mast BullBaod
LogicVat
Sihtttur
32.35%
3,78%
32%
MathVerse
LogieNica
MMSStr
HallBendi
(A) Accuracy vs. Reasoning Progress
(B) Error Category Distriburion
(C) RRasoonng-fndusee Induceed
Perception Eirors
Figure 2: The dual nature analysis of reasoning. In (A), we depict how accuracy evolves throughout the reasoning process. In (B), we show the distribution of error categories under full reasoning (inner ring), alongside the proportion recoverable via early decision (outer ring). In (C), we further present the initial ratio of perception errors on different benchmarks (left bar), as well as the remaining errors that persist after recovery (right bar). important to note that recoverable ratio is a loose metric based on series of early decisions along the reasoning trajectory rather than a single evaluation.
ing extended, step-by-step explanations. This shift has inadvertently amplified the forgetting issue, limiting the benefits of reasoning in multimodal contexts. To address this, we propose VAPO, training-basee based method that explicitly reinforces the model's perceptual grounding, effectively alle- viating visual forgetting, and achieving new state of the art across established benchmarks.
3
THE DUAL NATURE OF REASONING
3.1
NO-FREE-LUNCH DILEMMA: LOGIC vs. PERCEPTION
We first explore how accuracy evolves throughout the reasoning process in existing multimodal rea- soning models. Specifically, we select three representitve VLMs, i..., R R1-OneVision (Yang et al., 2025c), VLAA-Thinker (Chen et al. 2025) and Vision-RI (Huang et al. 2025), spanning both SFT- then-RL and RL-only training pipelines. We evaluate models on reasonin--centre benchmarks compassing Math Verse (Zhang et al. 2024) and Logic Vista (Xiao et al. 2024). and lision-intensive benchmarks including MMStar (Chen al., (024b) and HallusionBBenhh (Guan al., 2024). To enable fine-grained analysis, we introduce an early decision mode, in which the model is prompted terminate reasoning at intermediate logical boundaries such as commas, periods or line breaks, and provide an immediate answer, as illustrated in Fig. T This setting allows us to quantify the contribution of each reasoning segment to the overall task performance.
Longer reasoning does not guarantee better performance. As shown in Fig. 2 (A), we leverage early decision control the reasoning length and monitor accuracy trend while reasoning progresses from 0%, i.e., direct answering to 100%, i.e., full reasoning. In the early stages, reasoning yields substantial gains across benchmarks, consistent with prior works. However, as reasoning contin- ues, these gains tend to plateau and even begin to reverse, leading to decline in accuracy. This dual effect is particularly pronounced on benchmarks such as MMStar and HallusionBenchh, where accuracy drops by over 2% from the peak, almost offsetting the original benefits of reasoning.
The above analysis suggests that, while reasoning is overall effective, its potential constrained by hidden factor that acts as significant bottleneck. To further examine this limitation, we perform comprehensive error analysis. In particular, we classify incorrect responses of Vision-R1 under full reasoning into three categories, based on the characteristics of their reasoning trajectories:
1) Perception Error. Reasoning demonstrates notable deficiencies in the understanding and recog- nition of visual details such as chart misreading or hallucination exemplified in Fig.
2) Logical Error. Models exhibit symbolic reasoning errors such as arithmetic inaccuracies, logi- cally invalid inferences, incoherence across intermediate reasoning steps.
3) Knowledge Error. There are commonsense violations or factual errors that may stem from outdated knowledge and inherent limitations in the pre-training phase.
We employ GPT-5 (Singh et al. 2025) to identify error categories and quantify their distribution, Beyond this, we further investigate the impact of reasoning
with details provided in Appendix A.3
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0.30
Attention Raaio
Accmmcy (%)
0.30
0.10
Vanilla Brasuring
Meast Reploy
Varilla Resaoning
Repley
Inarttdd Poetion
Fooas Phorege
Insetted Pooiten
Toxrs Procage
dpoerts
dinserts
752
<Dnsert>
<meers
<ir <ireeets
<tonerts
100%
(A) Visual Attention Ratio vs. Generation Steps
(B) Accurncc vs. Reasoning Progress
Figure 3: The analysis of visual forgetting, In (A), we randomly select example from Math Verse and visualize the evolution of attention ratio to visual tokens from Vision-RI across generation steps. We consider vanilla reasoning as well as two variants, i.e., visual replay and focus prompt. In (B), we report the average accuracy across established benchmarks and models, and track its trend throughout the reasoning process under the aforementioned three settings. The average cutoff positions across examples to insert images or instructions are indicated in vertical dash lines.
progress on these failure cases. Specifically, we apply early decision, where an error case is consid- ered recoverable if the model can produce correct answer at any earlier point along the reasoning trajectory, and report the corresponding proportion of such cases. These recoverable instances sug- gest that the model is initially on the right path but ultimately misled by prolonged reasoning.
The harder the model thinks, the worse the model sees. As shown in Fig. (B), despite the com- plexity of these reasoning benchmarks, the majority of errors made by VLMs stem not from logical failures (33.08 (5)), but rather from basic perception errors (55. 23%). Remarkably, a substantial por- tion of these perceptual errors (32.35%) may be recovered by terminating the reasoning process in advance through early decision. This counterintuitive result suggests that as reasoning progresses, the model's perceptual capability gradually weakens. Together with the finding in Fig. (A), these degradation in visual perception caused by prolonged thoughts is most likely key contributor to the performance decline observed in the later stages of reasoning.
The harms of reasoning are most evident in vision-heavy tasks. In Fig. (C), we further examine the impact of reasoning progression on perception errors across different benchmarks. Notably, in MMStar and HallusionBench, which feature high-resolution waal-world images and perceptually elusive content, the proportion of perception errors arising during reasoning is substantially higher. Moreover, the share of recoverable instances via early decision in these benchmarks is markedly greater than that observed in others, such as Math Math and Vogic This observation suggests that, in contrast tasks with simple visual structures, e.g., geometry, the shortcomings of reasoning become more pronounced when applied to vision-intensive problems.
3.2
SIDE EFFECT OF REASONING: VISUAL FORGETTING
The above findings highlight a severe trade-off' between logical capability and perceptual grounding in multimodal reasoning, which has emerged as a major bottleneck in the development of VLMs. Therefore, to further probe the underlying causes, we track the evolution of attention ratio assigned to visual tokens each generation step, which provides a proxy of how visual information con- tributes to the reasoning process. As shown in Fig. 3 (A), under vanilla reasoning, the attention of visual tokens exhibits a marked decline as reasoning progresses, eventually reaching negligible levels. This pattern indicates the model's marision-maiing becomes increasingly driven by its own historical thoughts rather than the visual cues, a phenomenon termed visual forgetting. To validate this hypothesis, we design two straightforward inference-level remedies:
1) Visual Replay. Instead of presenting the visual input only at the beginning, we reintroduce the image to models at regular intervals throughout the reasoning process.
2) Focus Prompt. Similarly, at regular intervals, we explicitly prompt models to revisit the input image with instructions such "I need to see the image" or "I have to look back".
Encouraging models to look more often boosts reasoning. Back to Fig. 3 (A), we insert either the image instruction at four positions along the reasoning trajectory, which are approximately evenly spaced and aligned with logical boundaries; see Appendix(A.5 more details. Notably, both approaches trigger sharp and immediate increase in visual attention upon insertion. As shown in
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Fig. (B), this reinforcement of visual information alleviates the performance degradation observed during the reasoning process, with visual replay yielding particularly pronounced gains, ultimately outperforming vanilla reasoning by around 1.5%. This also empirically shows that visual forgetting the fundamental cause preventing reasoning from realizing its full potential in multimodal tasks.
4 THE PROPOSED METHOD
4.1 PRELIMINARY
To introduce our method, we first revisit the standard RL-based algorithm, typically Group Relative Policy Optimization (GRPO), which simplified variant of Proximal Policy Optimization (PPO) that eliminates the critic model. In the multimodal context, consider a dataset D, where each instance consists of a question q. ground truth answer y, and visual input The objective of GRPO is to encourage high-quality responses while penalizing inferior ones by comparing set of generated candidates. Formally, given policy model næ, we aim to maximize the following:
JGRPO ((o.v.T)DD.(.... Fo (.10.T)
G
[0.1
min (r,e(0)AA.,, clip (ra,(()), - e, e,1+)) ADKL (No |l (
G
Oi
with (0) = Th (oi q, 04.cc)
(1)
Toaaa (0i, 0i
denotes the number sequences o, sampled from the old policy Vold and À are hyperparameters that constrain the clipping bounds to ensure on-policy training and penalize deviations from the reference policy ref stabilize optimization. The estimated token-level advantage A,,, is derived
by broadcasting the normalized sequence-level reward R, which is defined as follows:
R; mean (R) i=
A
(2)
std (R)
where R = {R1, R2,1 R2...., Re} indicates the reward of the sequence group. The design of reward functions offers considerable flexibility. A widely adopted approach involves the of verifiable rewards, which evaluate model responses by comparing them to ground-truth answers to derive accuracy-based and format-consistency signals.
4.2 VISION-ANCHORED POLICY OPTIMIZATION
Although GRPO has become common practice for training reasoning models, findings in Sec- tion 3 reveal that existing VLMs suffer from visual forgetting. constituting a major bottleneck in multimodal reasoning. To address this, we propose VISION-ANCHOREI POLICY OPTIMIZATION (VAPC), a simple yet effective approach as a multimodal replacement for GRPO that steers the rea- soning process toward visually grounded trajectories. As shown Fig. 4 VAPO first generates set of correct and incorrect claims that describe the visual details in the images. These claims are strate- gically inserted visual anchors along the reasoning path, where the model prompted to judge their veracity, thereby enabling ongoing evaluation of model's perceptual capability throughout the reasoning process. Consequently, we introduce perception reward, aggregated over all anchor points, which explicitly incentivizes the model to retain and utilize visual cues during reasoning.
Generating Visual Claims. We use visual claims as a proxy evaluate the model's 's perceptual capability at various reasoning stages. To serve this purpose, the claims need to satisfy two criteria: I) balanced, i.e., the set should contain an equal number of correct and wrong claims avoid biased evaluation and 2) independent, i.e., the model judgment of each claim must rely solely on visual input, rather than historical reasoning. Specifically, for each example, we employ GPT-5 to generate diverse set of claims with balanced correctness. To enforee independence from reasoning contexts, we only provide visual input without revealing the corresponding question, thereby ensuring claim verification is grounded purely in perceptual understanding. See Appendix A.6 more details.

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Question: What the whesl the green bus?
-hhinkLLat's solve this probism step by step
<thinkoLet's solve this problom step by step
There are totally three long buses in the image.
There are totally three long buses in the image.
2. One green, and others are gray and purple
<anchor- The gray bus is facing toward the left.
3. The green bus positioned the middle left.
In this claim conrect Answer (yea/ne) yos
4. The rear wheel is the lefimost wheel the bus.
rollout
<answer> The wheel color is dark blue. s/answwur-
5. The wheel has dark blue color. v/trinke
<thinleLat's solve this problem
There
long green, and otherss gray purple.
image
VLM
<anchor- are
motorcycles.
gray is facing toward the left. (CORRECT)
correct? Answer (yes/not yes X
Thare arw three different matercycles (WRONG)
Thare ane one airplane
photo. (CORRECT) The green bike in the miodle right. (WRONG)
<thinkoLtt's solve this problem (previous contexts)
generate
The only small car in yellow. (CORRECT) ... [more visual claima)
insert anchors
vanchor- There
airpiane the
GPT-5
this ciaim correct? Answer (yes/ne]: X
Figure 4: The overview of VAPO. On the left, we first employ GPT to generate set of claims about visual input, each of which may be either factually correct or not. These claims are then treated as anchors and inserted into the model's reasoning process. Specifically, on the right, for each anchor, we randomly sample a prefix from the reasoning trajectory, append the claim to this truncated context, and probe the model judgment regarding the claim's validity.
Inserting Visual Anchors. Upon obtaining these claims, we set up series of visual anchors within the model's reasoning process, functioning as intermediate checkpoints. Once reaching the an- chor, the model is queried with selected claim to assess its perceptual capability at that stage of reasoning. Formally, given a trajectory og = (ot.1, O,.2,' ,
Oi,T), we define a set of anchors A = {01, (dg ,, aak with 0g E [1, T] denoting the position randomly distributed throughout the trajectory. At each anchor ag. claim C₁ E C, is sampled from the corresponding claim pool and
appended to the prefix reasoning context, yielding binary score over the judgement:
S
arg max [E(ess,o))
Toli 9, oi,ca,, ck) = l
(3)
where 1. denotes the ground truth of the claim. In other words, we evaluate the model's perceptual capability by probing its binary decision on the claim and scoring it against the reference label.
Perception Reward. Building on the anchors above, we introduce a perception reward that quanti- fies the model's overall perceptual capability throughout the reasoning process. Formally, given the &
per-anchor scores {8k) k=1- we design a late-emphasis weighted aggregation:
Rpere
Ek=n E Wk
SkSk
with wi exp B
ax T
(4)
where wa indicates the weight assigned to the corresponding anchor, and [ is a hyperparameter that controls the degree of emphasis of later anchors. This design is motivated by the observation that the model's perceptual capability tends decline reasoning progresses. Consequently, greater weight is assigned to anchors in the latter stages to precisely target the model weakness. In summary, the final reward for a sampled sequence o, is computed as:
R²
Race Riut + ? [Race 1] Rperc:
(5)
where Race and Rime denote accuracy and format reward, 7 is the weight of perception reward. In practice, we impose an accuracy condition on perception reward to guard against reward hacking, thereby preventing models from simply boosting perceptual capability by producing trivially short reasoning paths. Our training procedure follows the objective of GRPO as defined in Section 4.1
5 EXPERIMENT AND RESULTS
Implementation Details. Unless otherwise specified, all models are trained on ViRI.9KK (Wang ct al. 2025a), high-quality and comprehensive dataset, for 2 epochs with learning rate of 5e We adopt Qwen2.5- V- (Bai et al. 2025) with 3B and 7B parameters as base models. For group reward computation, we follow previous works (Huang et al. 2025 Chen et al. 2025) by generating 5 responses per example with a sampling temperature of 1.0. For VAPO, we set the default number of anchors to K- 20, the late-emphasis weight to B 1.5, and reward weight y 0.1.
Benchmarks. We denote the models trained with our method as ten benchmarks covering diverse task types: mathmatical benchmarks such as Math Verse (Zhang
and evaluate on
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Models
Math Verse
Math Vista
Math Vision
LisicVista Vista
WcMath
Geometry3k
Avg.
Close-source modely --hhin-Th
81.2
81.9
72.0
70.0
71.1
79.9
76.1
Demini-2.5-Pro
76.9
80.9
69.1
73.8
78.0
77.2
75.9
Open-souree models
Qwen2.5 5-BBBBB
40.7
62.3
23.2
42.6
33.1
38.5
40.1
Intern *
34.5
68.2
25.6
38.3
38,6
44.8
41.7
L.-OneVisii--T7
46.4
64.1
29.9
45.6
44.6
46.1
46.1
VI.AA-Thinke--71
48.2
68.0
26.4
48.5
41.5
50.6
47.2
Vision-R1-
52.4
73.5
28.2
49.7
41.6
49.0
49.1
Our models TAPO-Thinken VAFO-Thinker-72 53.3
35.8
67.1
23.9
39.7
35.4
44.2
41.0
75.6
31.9
50.9
43.6
51.3
51.1
The evaluation of our proposed method various mathematical benchmarks. We report results of existing open-source multi-modal reasoning models, well as proprietary models for reference. Note that indicates baseline results referenced from the OpenCompass leaderboard.
Table 1:
Models
MMMU
MMStar
Hall
MMVet
Avg.
Models
WeMath
Geo3k
MMStar
Hall
Avg.
Qwen-Ve VL
52.7
50.0
64.8
VLAAFp
41.8
50.7
51.1
55.2
49.7
RI-OV
54.3
54.1
52.5
65.2
56.5
VLAAvR
42.3
51.1
52.9
56.2
50.6
VLAA
59.1
49.7
54,7
70.0
58.4
V-RIe>
42.1
49.7
61.8
50.5
51.0
V-RI
57.6
61.4
49.5
71.1
59.9
V-RIvR
42.5
50.5
62.1
51.8
51.7
VAPO
60.2
63.0
57.4
71.9
63.1
VAPO
43.6
51.3
63.0
57.4
53.8
Table The evaluation our proposed method on general-pung purpose benchmarks. All baselines considered this evaluation are of the 7B scale.
Table 3: Comparison with strong baselines aug- mented with test-time remedies. FP and VR de- note focus prompt and visual replay, respectively.
et al.
2024), MathVista (Lu et al.
2023), MathVision Wang et al. 2024b), Logic (Xiao et al.,
2024.. WeMath (Qiao al.) (024) Geometry3k (Lu et al.] [2021], p. as well as general-purposs
ones including MMMU (uue et al.
2024), MMStar (Chen et al. 2024b), HallusionBench (Guan)
al. 2024) and MMVet Yu al. 2023). Further evaluation results are provided in Appendix
Baseline Methods. To verify the effectiveness of our approach, we compare VAPO-Th Thieer nnke with a range of existing strong reasoning models, including proprictary ones such as GPT-5- Thinking (Singh et al.) (2025) and Gemini-2.55Pro (Comanici al.) 2025), as well open-source counterparts encompassing InterVVL2.5 (Chen et al., (024d), RI-One Vision (Yang et al. (025c), VLAA-Thhnke (Chen et al., 2025), and Vision-RI (Huang al.) (025). The baseline results are pri- marily referenced from the corresponding papers, secondarily from the OpenCompass leaderboard, and reproduced when neither source is available. See Appendix A.7 for configuration details.
5.1
MAIN RESULTS
VAPO consistently improves accuracy across diverse benchmarks. As shown in Table I VAPO-Thin 1PP0-Tinnke--75 outperforms recent reasoning models of same scale on mathematical problems, achieving average improvement of 2% (49.1% - 51.1%). The advantage is more pronounced on purpose tasks, as shown in Table 2 where our method surpasses previous best results by 3.2% (59.9% 63.1%), thereby establishing compared to logic-heavy problems such as math, VAPO is particularly effective on vision-intensive tasks like MMStar and HallusionBench, demonstrating stronger visually grounded reasoning.
state of the art. This suggests that
VAPO offers a principled solution beyond inference-level remedies. In Section 3.2 we introduce remedies, i.e., visual replay and focus prompt, preliminary exploration verify the visual forgetting issue. Here we compare VAPO against these test-time strategies. As shown in Table 3 even when strong baselines are equipped with visual replay or focus prompt, VAPO still achieves substantially higher performance, suggesting that inference-level remedies alone are insufficient to address visual forgetting, whereas VAPO fundamentally rectifies this reasoning deficiency.
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0.30
Accomcy (%)
4
0.30
Attention
0.10
Veten-Ri-71
Vaice-ft--
VAPO-Thinkee-TI
VAPO- 7a-
100
150
201
250
200
40/
000%
80%
10W
(A) Visual Attention Ratio vs. Generation Steps
(B) Accurney vs. Reasoning Progress
Figure 5: Zoom-in analysis of our method. In (A), compare the evolution of visual attention across generation steps between VAPO and the baseline on the selected example. In (B), we track the average accuracy across ten benchmarks throughout the reasoning process via early decision.
Asstgge Asanay (N)
Pecegton teouet
Tratning Steps
Touiring Stope
Andhur-Nurbber
Fitg-iigttasss Weight
Figure 6:
The learning curves of accuracy and
Figure 7: The ablation study on the effects of anchor number K and late-emphasis weight (3.
perception reward during the training stage.
5.2 FURTHER DISCUSSION
VAPO fully releases the potentials of reasoning. Here we analyze the evolution visual attention and accuracy the course of reasoning process. In Fig. (A), compared with the baseline, model demonstrates more gentle decline in the attention ratio, sustaining a consistently higher level in the later stages, indicating that VAPO effectively strengthens the contribution of visual cues to model decisions. The benefit brought by this is directly reflected in accuracy, as shown in Fig. 5 (B), where in contrast to the baseline which exhibits sharp performance decline during later steps. our method achieves steadily increasing accuracy, thereby fully leveraging the advantages of reasoning.
VAPO greatly enhances perceptual capability during reasoning. In Fig. 6 we plot the training curves of accuracy and perception reward. The results reveal that perception reward exhibits steady upward trend in parallel with accuracy, suggesting that the model progressively allocates greater attention to visual input and thereby improves its ability to verify visual claims. Interestingly, an sharp decline in perception reward is observed during the early stage, which may be attributed to the initial dominance of accuracy and format alignment, causing the model to temporarily disregard visual information. This underscores a fundamental limitation prior standard training paradigms.
5.3 ABLATION STUDY
More anchors yield better visually grounded reasoning. We examine the impact of visual an- chor number K average accuracy across ten benchmarks. As shown on the left of Fig. 7 vary from 0, which degrades to vanilla GRPO training, to 20, which is adopted as our default configuration. The results indicate that the average accuracy improves rapidly K increases and approaches saturation at around = 20. This behavior is intuitive, as setting up denser anchors during reasoning provides more reliable assessment the model's perceptual capability.
Later anchors contribute to greater gains. In VAPO, the parameter 3 controls the relative empha- sis placed later anchors target longer reasoning, As shown on the right of Fig. 7 we evaluate the effect of varying 3 on average accuracy. When 3 = 0, all anchors are assigned equal weights, while larger values assign greater importance to later anchors. The results show that performance peaks at 3 = 1.5, where around 50% of the total weight is concentrated on the last 30% of anchors. More results such as ablation reward weight y and limitation analysis are provided in Appendix

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Published as a conference paper at ICLR 2026
6 CONCLUSION
In this study, we conduct an investigation into the capabilities and limitations of multimodal reason- ing. We first reveal the dual nature of reasoning: while it enhances logical inference and proves bene- ficial for challenging problems, longer reasoning length may gradually impair perceptual grounding, leading to recognition deficiencies on otherwise basic visual questions. Then we further identify this phenomenon and attribute it to visual forgetting, where prolonged reasoning length causes the model to disregard visual information. To address this, we propose VISION ANCHORED POLICY OPTI- MIZATION (VAPO), a simple yet effective method that steers the reasoning process along trajectories anchored in visual evidence. Our result model, VAPO-Thi n 7B, effectively mitigates visual forgetting and establishes a new state of the art across various benchmarks.
VISHON-ANCHORED
ACKNOWLEDGEMENT
This research was, in part, funded by the U.S. Government - DARPA TIAMAT HR001 HR00112290427 12490421. The views and conclusions contained this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. We also gratefully acknowledge Lambda GPU Cloud and Maincode for their generous provision of computational resources.
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CONTENTS
EXPERIMENTAL DETAILS
17
A.1
BENCHMARK STATISTICS
17
BASELINE SETTINGS
17
A.3
ERROR ANALYSIS
18
A.4
ATTENTION VISUALIZATION
19
A.5
INFERENCE-LEVEL REMEDIES
20
A.6
VISUAL CLAIM GENER CENERATIOON
20
TRAINING CONFIGURATION
21
B
MORE EVALUATION
22
ERROR CATEGORY DISTRIBUTION
22
B.2
VISUAL ANCHOR SCORE
22
B.
3
PERCEPTION REWARD WEIGHT
22
ABLATION ON DATA AUGMENTATION
23
B.5
EFFECT VISUAL CLAIMS
24
B.6
KL PENALTY COEFFICIENT
24
7
VAPO WITH INFERENCE LEVEL REMEDIES
24
B.8
8
ATTENTION-BASED REWARD
25
B.9
COMPUTATIONAL EFFICENCY AND COST
25
LIMITATION ANALYSIS
26
B.
CLAIM LABEL QUALITY
26
B.
METHOD SENSITIVITY TO OTHER GENERATORS
27
13
VAPO ON OTHER MODELS
27
B.
14
EVALUATION ON MORE TASKS
28
15
VAPO WITH SELF- SUPEVISSED ANCHORS
28
SINGLE-CUT RECOVERABLE RATIO
29
B.17
DATA DEDUPLICATION CHECK
29
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B.18
EFFECT OF ACCURACY GATE
30
B.19
EFFECT OF ANCHOR PLACEMENT STRATEGY
30
20
COMPARISON WITH MORE BASELINES
31
B 2
EFFECT OF VAPO ON LONG-THOUGHT EXAMPLES
31
B.22
REASONING LENGTH DISTRIBUTION
B. 23
VAPO ON MULTI-IMAGE TASK
32
BROADER SOCIETAL IMPACTS
32
D.
SUPPLEMENTARY RESULTS
33
D.I FULL NUMERICAL MAIN RESULTS
33
D.2
NUMERICAL RESULTS OF K
33
D.3
NUMERICAL RESULTS OF
D.4
FULL RESULTS OF AUGMENTED BASELINES
33
D.5 FULL RESULTS OF 3B PARAMETER SCALE
33
16

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A
EXPERIMENTAL DETAILS
A.1 BENCHMARK STATISTICS
In the main paper, we provide a high-level overview the selected benchmarks. Here, we present detailed information for clarity and ease of reproducibility, including dataset types, sizes, and splits. All evaluation scripts are implemented using the VLMEvalKit framework (Duan et al. (024).
1) Math Verse is a benchmark comprising 2.612 high-quality mathematical questions. Each example provides varying levels of multimodal information. Following prior work, we evaluate on the Test Mini split using the Vision Only setting, which includes approximately 700 samples.
2) Math Vista is a comprehensive mathematical benchmark that evaluates a range of skills including puzzle solving, algebraic reasoning. and scientific understanding, and comprises 6,141 examples. For our evaluation, we use the Test Mini split, which contains approximately 1,000 examples.
3) Math Vision comprises 3,040 high-quality mathematical problems sourced from real-world math competitions, spanning 16 distinct disciplines and five levels of difficulty, providing a comprehensive and challenging benchmark for evaluating VLMs. We conduct our evaluation using the full test set.
4) Logic Vista assesses the fundamental logical reasoning capabilities of VLMs, covering a range of reasoning types including spatial, deductive, inductive, numeric, and mechanical. The benchmark comprises 448 visual multiple-choice questions. We conduct our evaluation on the full test set.
5) We.ath comprises 6.5k visual math questions structured around 67 hierarchical knowledge concepts across five levels of granularity. We evaluate on the Test Mini split, which contains ap- proximately 1,740 examples, and report the strict score as the primary evaluation metric.
6) Geometry3k consists of 3,002 geometry problems with dense annotations in formal language, requiring abstract groblem-solving and symbolic reasoning based on axiomatic knowledge. For evaluation, we combine the validation and test splits, resulting in approximately 900 examples.
7) MMMU a challenging benehmark that covers broad range of disciplines, requiring college- level subject knowledge and reasoning. It contains 11.5k curated multimodal questions sourced from college exams, quizzes, and textbooks. We perform our evaluation on the validation split.
8) MMStar is vision-indispennabll multimodal benchmark specifically designed to ensure that each sample exhibits strong visual dependency and requires advanced multimodal reasoning capa- bilities. It comprises 1,500 samples for offline evaluation. Here we evaluate on the full test set.
9) Helch is designed to challenge advanced VLMs by emphasizing fine-grained under- standing and interpretation of visual information. It consists of 346 manually curated images paired with 1,129 questions. In this study, we conduct our evaluation on the full test split.
10) MM comprises 218 examples and defines six core vision- vision-language capabilities, focusing on their integration to evaluate the synergy among different skills. It is designed to assess the overall competence of generalist models. We perform our evaluation the full test split.
A.2 BASELINE SETTINGS
In the main text, we compare our method against several popular baselines to validate its effective- ness. For models such as GPT-5-Thinking, Gemini-2.5-Pra and Intern we directly report results from the OpenCompass leaderboard. For other models, we provide reproduced results when official numbers are not available in the original papers. Below, we detail the settings used for these reproduced baseline models for reference. All results reproduced using greedy decoding.
1) RI-One Vision includes both 3B and 7B variants, which are first trained via SFT followed by RL, with particular focus on mathematical reasoning tasks. In our experiments, adopt the publicly available 7B checkpoint based on Owen2.5-VL-7B.
2) VLAA-Thinker is RL-only model trained high-quality and challenging dataset, and is the first to demonstrate that RL outperforms SFT in multimodal settings. In our experiments, we use the VLAA- A--Thinker-7B variant, which is also based Qwen2.5-VI-7B..
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ROLE
You are an impartial evaluator. Given an image, a question, the ground truth answer, and model's reasoning traces, identify the PRIMARY error category in the reasoning process.
FRROR TYPE (CHOOSE ONE PRIMARY)
A) Perception Error: Failure correctly perceive, localize, or read visual content (e.g., chart misreading. counting mistakes, OCR/label misread, visual hallucination/attriinn errors).
B) Logical Error: Symbolic'algorithmiic reasoning mistakes given correctly perceived facts (e.g., arithmetic slip, invalid inference, algebraic misstep, inconsistent intermediate steps).
C) Knowledge Error: Missing'incorrett factual or commonsense/world knowledge not visually available (or clearly contradicted by known facts), including outdated knowledge or misremembered facts.
D) Others: No observable in reasoning, the reasoning content too short uninterpretable.
OUTPUT
Return exactly one character: A, B, o D. Do not include any other text, spaces, punctuation, or quotes.
Figure 8: The designed prompt to instruct GPT-5 to assist in categorizing the different error types.
3) Vision-R1 is powerful reasoning model trained at scale, with the combined amount of RI. and SFT data being approximately five times larger than that used our setting. In our experiments, we use Vision-R1-7B, which represents the strongest variant reported by the authors.
A.3 ERROR ANALYSIS
conduct comprehensive ferent types of errors and quantify their distributions, aiming to assess the impact of reasoning on failure cases. The prompt used for GPT-5 input is shown in Fig. B As illustrated, we provide GPT-5 with four options: three predefined error categories, along with an Others category to account for small number of outliers, such as cases with uninterpretable content. We then compute the pro- portion of each еπтог type to support our analysis. For completeness, in addition to the perception error examples as shown in Fig. I here we also provide representative cases of logical errors and knowledge errors, as illustrated in Fig.
In Section 3.1
analysis by leveraging GPT-5 categorize dif-
In addition, based on the recorded human audit of 500 randomly sampled failure cases, we find that instances containing multiple error types within the same response are quite rare, occurring in fewer
Trigonometry
Logical Error (Arithmetic)
Q: In the image, find sin A. GT Answer: 0.29
<think Okey, to calculate the sine value of ZA, need to think step by step.
BC+6, AC-20, triangle ABC right triangle. 2. To derive sin A, we first need to determine the length of hypatenuse AB.
From the image,
3. Given BC and AC,
derive AB BC++AC*
4. Substitute the numbers into the equation, we have AB? - 400 436 square root
v436
6. By definition, sine given by the relio of the opposite side to hypotenuse +/thnkk
Therefore,
value of BC/AB 6/24.68
<answer> The sine function of LA is equal to 0.24 s/answer>
Movie Recognition
Knowledge Error (Commonsense)
Q: From which film this photo taken? GT: Titanic
think- asked identify film
photo.
The image features man and woman standing on the deck of ship.
behind 3. The woman has a red hair, and is wearing a vintage dress.
smiling
The
the background and the clathing imply sailing vessel.
5. The sunset palette and romantic pose suggest a fantasy genre.
Based the ship-like setting
dramatic lighting, might conclude
this scene from Pirates of the Caribbean: Dead Man's Chest. </think>
<answer-Th film Pirates of the Caribbean: Dead Man's Chest s/answer>
Figure 9: The representative examples logical and knowledge errors.
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Error Category
Logic + Pere
Pere
Logic
Knowledge
Pere
Knowledge + Logic
Examples
3
Table 4: The number of examples with multiple error categorics co-occuring.
than 20 examples (< 4%), which is also the reason we prompt GPT to directly identify the primary error category as shown Fig. 8 To further investigate this, we manually re-examine these cases and record the specific ordering patterns in which different enror categories co-occur. As shown in Table any error category combinations not listed above did not occur in our samples. Notably, cases which logical errors precede perceptual errors are extremely rare. This rarity ensures that such patterns do not meaningfully affect our experimental results or conclusions. It also makes difficult to draw any substantive connection between perceptual errors and other error types, given how infrequently they co-occur in practice. These results further reinforce our finding that longer reasoning contexts tend to induce visual forgetting, which in turn leads to the observed performance degradation.
A.4 ATTENTION VISUALIZATION
In the main paper, we visualize the attention over image tokens reflect the contribution of visual information to the model's reasoning process. Here, we provide additional details on the implemen- tation. For each generation step, we compute the sum of attention scores after softmax assigned by the output token to all preceding image tokens. This yields ratio between O and 1, which is then averaged across all layers to produce the final attention value. Additionally, Fig. (A), since inserting images, i.e., visual replay, instructions, i.e., focus prompt, may alter the final reasoning trajectory and result in slightly different sequence lengths, we normalize the comparison by truncat- ing all outputs to the first 250 tokens, which basically covers the full response. This strategy is also applied in Fig. 5 (A) when comparing the visual attention between Vision-RI and VAPO-Thinkes
ROLE
You are given an image. Generate EXACTLY 20 DISTINCT visual claims about THIS image.
DEFINITION: A visual claim is short, factual statement about something that is CLEARLY VISIBLE in the image (no guesses, no context outside the image).
HARD CONSTRAINTS:
Each claim should be
than 20 words.
Each claim must be obviously verifiable from the pixels.
One fact line (no conjunctions; no explanations).
English only; avoid hedges ("maybe", "appears"" "likely").
CORRECTNESS:
Produce 10 CORRECT and 10 WRONG claims (total 20).
Wrong claims must plausible and possible perception
Do not make logically impossible statements (e.g., "left of itself").
Correct and wrong claims should be independent, not just simple opposites of each other.
Label each correct claim with [CORRECT] at the end of the line.
Label each wrong claim with [WRONG] at the end of the line.
OUTPUT FORMAT (MUST MATCH EXACTLY):
Retum ONLY the numbered lines, nothing else. Numbering style: "1. "I...." through "20....
One line per claim.
Figure 10: The user prompt for GPT-5 generate visual claims for VAPO.
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A.5 INFERENCE-LEVEL REMEDIES
In the main text, we introduce two inference-level remedies, Le., visual replay and focus prompt. preliminary strategies demonstrate the negative impact of visual forgetting on reasoning. For completeness, we provide a detailed description of both approaches here.
In the visual replay strategy, the input image is reintroduced periodically during the model's rea- soning process at regular intervals. In practice, to improve efficiency and prevent exceeding the model's context length, the reinserted image is downsampled to a lower resolution. Furthermore, the insertion points are selected to satisfy two criteria: (1) uniform segmentation of the reasoning trajectory, and (2) alignment with logical boundaries such as commas, periods, or line breaks to avoid interrupting syntactic or semantic units.
For the focus prompt strategy, at each insertion point, we randomly sample one prompt from a set of three manually designed instructions including "I need to see the image". "I have to look back" and "Let me verify against the visual imput", to ensure robustness against prompt variability. For alignment, we adopt the nearby insertion positions as used in visual replay, facilitating a consistent comparison between the two methods. It is worth noting that in visual replay, the image is reintro- duced part of the user prompt in dialogue format, whereas in focus prompt, the instruction is directly injected into the assistant's 's response.
A.6 VISUAL CLAIM GENERATION
Our method encourages the model to rely more effectively on visual input by introducing a percep- tion reward, which is derived from the model's s ability to evaluate set of visual claims during the reasoning process. This evaluation serves as proxy for assessing the model's perceptual capability at varying reasoning stages. To support this, we prompt GPT-5 to generate specified number of visual claims conditioned on the input image. An example of the user prompt used for this purpose is shown in Fig. [O] We require the generated visual claims to be concise factual statements, free from hedging terms such as possibly or appears. Each set of claims must contain an equal number of correct and incorrect statements. Furthermore, the claims are constructed to be independent of the corresponding example question, ensuring that the model must refer to the visual input rather than relying on prior outputs when evaluating their validity. While several generated visual claims have already been presented in Fig. we provide more illustrative examples in Fig. 11 for reference.
In addition, employ rule-based filtering mechanism and dynamically generate visual claims. That is, for each training example, any claim that fails to meet the required criteria is discarded and resampled until the target number of valid claims is reached. The filtering process incorporates
1. 1.Segment BD is parallel to the segment BC. [WRONG]
BC is a vertical line segment in the triangle. [WRONG]
Point C is at the bottom-right of the triangle. [CORRECT]
M
4. The label M is on the left outside the triangle. [CORRECT]
D
5. The letter A is at the top vertex of the triangle. [CORRECT]
N
6. The right-angle square marks the angle veriex B. [WRONG]
B
1. Many bus windows are shattered. [WRONG]
2. The bus sits on a black flatbed trailer. [CORRECT]
3. The direction of the bus faces the camera. [WRONG]
4. The seene is captured on narrow city street. [WRONG]
5. The sky is filled with gray and thick clouds. [CORRECT]
6. The rear of the bus displays the number "1853". [CORRECT]
Figure 11: The examples of the GPT-generated visual claims.
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multiple constraints, detailed in the prompt specification in Fig. 10 including limits claim length, the prohibition of ambiguous expressions (e.g.. "maybe", "appears"), and the requirement to maintain balanced ratio of correct and incorrect claims. To reduce redundancy, compute pair- wise embedding distances between generated claims using Sween3-Emeddiin--0
(Zhang et al.) 2025). and remove claims with excessively high semantic similarity (> 0.95). We allow straightfor- ward low-level claims such as color naming, while we impose a mild diversity constraint that limits the number of purely low-level color counting statements per image through regular expression, favoring richer distribution of relational, text-based, and global scene-level claims.
A.7 TRAINING CONFIGURATION
Section 5, we briefly outline the training setup. Here, we provide more detailed description. By default, we adopt ViRL39K as training dataset, which is refined collection derived from multiple existing sources such as MM-Eureka (Meng al., 2025). MV-Math (Wang et al. 2025c), and M3CoT (Chen et al. 2024c). The dataset covers wide spectrum of domains, including STEM subjects, social topics, chart reasoning, and spatial relations. It is worth noting that prior baselines rely substantially larger datasets that combine SFT and RI, for example, 155k samples for R1- One Vision and 210k samples for Vision-R1. Although this comparison is not fair for us terms of training data scale, further highlights the effectiveness of our proposed method.
During the rollout phase, we sample responses per example with a temperature of 1.0, and employ vLLM the backend accelerate decoding. We then insert visual anchors into these generated responses to evaluate the model's perceptual capability. For each anchor, we randomly sample prefix of the reasoning content, append visual claim, and instruct the model to judge its correctness. To ensure binary decision-making the decoding process is constrained to produce either yes or no, with the temperature fixed at 0.0. Importantly, throughout this stage, we adopt the default system prompt of Qwen2.5-VL. without explicitly introducing the presence meaning of anchors. This design avoids altering the model's inherent behavior and keeps anchors fully transparent, serving as an implicit reward mechanism that encourages the model to rely more heavily on visual cues.
For training, we employ NVIDIA 1100-80G GPUs. The policy loss follows the default configu- ration of GRPO, where the clip ratio is to 0.2, and the KL penalty coefficient À is fixed at 1e 2 For VAPO, we set the anchor number K 20, the late-emphasis weight / = 1.5, and the perception reward weight 0.1. The entire training process is conducted using verl (Sheng al. 2025
Pescaption Einee
Kaowledge Enor
Frror Ratio (%)
45
15
Math Verse
Visia
MMStar
HallusionBench
Figure 12: The error ratio of Vision-RI as well as the correction rate achieved by our method across benchmarks. the bar chart, the full height of each bar represents the overall error rate of Vision- R1, with the light-colored segment indicating the proportion of errors successfully corrected by our method, and the dark-colored segment corresponding to the remaining uncorrected errors.
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Math Verse
Logic
Geometry3k
MMMU
MMStar
HallBench
Avg.
0.00
44.2
44.4
44.5
53.6
55.2
51.2
49.0
0.05
47.7
46.3
48.2
55.5
58.6
53.7
51.7
0.10
48.9
47.3
47.7
56.7
59.1
55.5
52.5
0.15
48.4
46.7
48.1
56,2
59.6
56.7
52.4
0.20
47.9
46.6
47.4
55.7
59.4
54.2
51.9
Table The benchmark accuracy with varying perception reward weight 7,.
B
MORE EVALUATION
B. ERROR CATEGORY DISTRIBUTION
In the main text, we analyze the error cases of the representative baseline Vision-RI and observe that perception errors constitute the majority, underscoring the detrimental impact reasoning on visual information utilization. To assess the effectiveness of our approach in addressing this issue, further examine the correction rate of our method across the different error categories of the baseline model, illustrated in Fig.
We observe that, for cases where the baseline model fails, method substantially corrects significant portion perception errors, with improvements reaching up 20% iistoniitenssive benchmarks. This perceptual correction capability constitutes major source of the performance gains achieved by our approach.
B.2
VISUAL ANCHOR SCORE
Our method assesses the model's overall perceptual capability by setting up visual anchors dif- ferent stages of the reasoning process. As shown in Fig. 6 the perception reward exhibits clear upward trend throughout training, indicating that the model becomes increasingly proficient at eval- uating visual claims and progressively relies more visual information. To further investigate the role of individual visual anchors, we visualize the distribution of anchor scores before and after training. i.e, comparing the base model with the model after applying method. As shown in Fig. [3] the base model, i.e., Qwen2.5-VL-7 exhibits a clear downward trend in anchor scores as reasoning progresses, eventually approaching the level of random guessing (around 0.5). In trast, our trained model significantly improves anchor scores, maintaining stable level around 0.9 throughout the reasoning process. This indicates substantial enhancement in perceptual capability as result of our method.
B.3 PERCEPTION REWARD WEIGHT
In the main text, we have conducted ablation studies on the anchor number and the late-emphasis weight 3 to identify their optimal settings. Here, we further investigate the impact of perception reward weight on model performance. For computational efficiency, in this experiment we do not
3.8
6.8
Average
2.6
0.6
Qwmn.5-VLL VL--B
Dwan2.5-V1-77
VAPO-Thinie-788
VAPO-Thukee-71
15
20
10
Anchor Position
Anchor Position
(A) Vatheerse
(B) MMStar
Figure 13: The anchor score across varying anchor positions, where a higher index corresponds to later stage the reasoning process. Scores are averaged across all examples within benchmark.
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Method
Math
Logic Vista
Geometry3k
MMMU
MMStar
HallBench
Avg.
GRPO
44.2
44.4
44.5
53.6
55.2
S2.2
49.0
GRPO uug
44.7
45.2
43.9
54.2
54.8
53.1
49.3
VAPO
45.9
46.1
46.7
55.2
57.8
54.3
51.0
Table 6: The comparison with VAPO and GRPO augmented with visual claim examples.
Method
Math Math
Logic
Geometry3k
MMMU
MMStar
HallBench
Avg.
GRPO
44.2
44.4
44.5
53.6
55.2
51.2
49.0
Non VisualClaim
45.7
44.9
44.3
54.8
57.2
51.9
49.8
QAlignedClaim
44.9
45.8
46.1
55.3
57.8
53.5
50.6
VisClaim
48.9
47.3
47.7
56.7
59.1
55.5
52.5
Table 7: The comparison with non-visuallly-eeeendent claims and question-aligned claims.
train on the full dataset but instead randomly sample 5000 examples from the full 39k training set. As shown in Table 5 setting 7 reduces the training procedure vanilla GRPO without percep- tual supervision. As increases, the average accuracy across benchmarks improves significantly, peaking around y 0.1. Notably, mathematical tasks tend favor smaller values of % which is intuitive given the relatively simple visual structures of these tasks. In contrast, isston-intensive benchmarks such as MMStar and HallusionBench benefit from more aggressive settings, reflecting their greater reliance strong perceptual capability.
B.4 ABLATION ON DATA AUGMENTATION
In our approach, visual claims play central role by providing reward signals that explicitly en- courage the model to rely more heavily on visual inputs. This mechanism helps mitigate visual forgetting and improves accuracy across benchmarks. However, since these visual claims are gener- ated by GPT and accompanied by correctness labels, they may be viewed as new synthetic examples augmented to the original training data. This raises critical question: are the performance gains brought by VAPO primarily attributable to the reward-driven promotion of visually grounded rea- soning, or are they simply a result data augmentation through the inclusion of these synthetic visual examples?
To investigate this question, we introduce a new baseline in which the generated visual claims are directly transformed into additional training examples and integrated into the original dataset. For computational efficiency, we randomly sample 5000 examples from the full dataset, as described in Appendix B.3]] For each example, generate 5 visual claims, resulting in fivefold expansion of the training set to 30k examples. This setup allows us to isolate the performance gains attributable purely data augmentation with synthetic visual claims. The baseline is trained using the standard GRPO algorithm without any additional modifications. In contrast, under the VAPO framework, these same visual claims are used as anchors during training, with the anchor number to match the available number of claims per instance.
K 5
As shown Table 6 augmenting vanilla GRPO with additional examples constructed from visual claims yields only marginal improvement over the original dataset (49.0 + 49.3). In contrast, our method achieves a substantial performance gain of 2% (49.0 - 51.0). This discrepancy may be attributed to two key factors: 1) The visual claims are generally short, simple, and strongly dependent on visual information, often solvable without long reasoning trajectories, thus offering limited learning capacity: 2) The augmented examples are derived from small number of unique images, ie.. five examples share the same image, significantly reducing data diversity and increasing the risk of overfitting. These results suggest that interpreting the role of visual claims purely form of data augmentation is not appropriate. Rather, the performance gains of VAPO are primarily driven by its promotion of visually grounded reasoning.
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(le
Math Verse
Logic Vista
Geometry3k
MMMU
MMStar
HallBench
Avg.
48.1
47.1
47.3
57.1
59.3
55.3
52.4
48.9
47.3
47.7
56.7
59.1
55.5
52.5
48.3
47.0
47.9
57.2
58.4
54.9
52.3
48.1
46.2
48.3
57.1
59.8
54.3
52.3
Table 8: The impact of KL penalty coefficient to the benchmark accuracy.
Method
Mathe
LigicVista
Geometry3k
MMMU
MMStar
HallBench
Avg.
VAPO 53.3
50.9
51.3
60.2
63.0
57.4
56.0
VAРOрP 53.1
51.2
50.7
60.5
63.4
57.9
56.1
VAPOVR
53.7
50.4
51.8
57.8
62.7
58.2
55.8
Table 9: The impact of VAPO augmented with inference-level remedies.
B.5 EFFECT OF VISUAL CLAIMS
In our method, visual claims serve critical proxy for measuring model's perceptual capability, To better understand their influence, we investigate how different types of visual claims affect model performance. In addition to our default setting, we consider two alternative variants: 1) non-visuully- dependent claims: although these are factual statements about the image, they emphasize external knowledge logical reasoning rather than concrete visual details; 2) question-aligned claims: these closely related to the specific question associated with the image, potentially allowing the model to assess claim correctness based on own historical reasoning outputs rather than pure visual grounding. To generate these claims, prompt GPT accordingly: for the first variant, explicitly instruct GPT to produce claims that are not visually dependent; for the second, we provide both the image and the corresponding question, asking output claims relevant to the question content. For efficiency, we conduct the analysis using 5000 examples sampled from the full training set.
As shown in Table both the wann--iually--eeeeeeee stantially worse than our default visual claim setup. This is likely due to the fact that non-visually- dependent claims can often be judged correctly without requiring strong perceptual capability, thereby diminishing the core purpose of VAPO. On the other hand, question-aligned claims are highly correlated with the question content, allowing the model infer their correctness from its own reasoning traces without relying on visual input. These results highlight that the design of visual claims crucial to the effectiveness of the VAPO framework.
and question-aligneds variants perform sub-
B.6 KL PENALTY COFFFICIENT
In the previous sections, we have analyzed the impact of VAPO-specccie hyperparameters, including the anchor number K, the late-emphasis weight 3, and the perception reward weight 7. For the underlying GRPO framework, we adopt the default setting for the KL penalty coefficient À = 1e For completeness, here we also examine the effect of varying on the performance of VAPC. To maintain computational efficiency, we conduct this analysis on a randomly sampled subset of 5000 examples from the full training set. As shown in Table g VAPO exhibits minimal sensitivity to the choice of A. Regardless of whether the KL penalty is applied varied in magnitude, the average accuracy fluctuates by no more than 0.2%.
B.7 VAPO WITH INFERENCE- REMEDIES
To further compare the effectiveness of our method with inference-level remedies, iee. visual re- play and focus prompt, we augment VAPO by incorporating these two straightforward approaches. We follow the same insertion strategy in Fig. 3 where interventions are applied at four points throughout the reasoning process. As shown Table augmenting our model with inference-leved remedies for visual forgetting results in minimal impact, with performance fluctuations within ap- proximately 0.2%. This suggests that our method has already substantially enhanced the model's reliance on visual input, effectively subsuming the benefits provided by these additional strategies.
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Method
Math Verse
LogicVista
Geometry3k
MMMU
MMStar
HallBench
Avg.
GRPO
48.2
45.5
47.3
56.6
58.9
53.2
51.6
VAP Catm
47.6
45.8
46.4
57.3
59.4
54.8
51.8
VAP Oper
53.3
50.9
51.3
60.2
63.0
57.4
56.0
Table 10: The comparison with attention reward which directly maximizes visual attention ratio.
Method
Epoch
Time
Accuracy
Gain
GRPO
18h46m
51.54
DAPO
2
25h1 1m
53.17
+1.63
VAPO
2
19h14m
55.91
+4.337
Table 11: The time costs and gains for different policy gradient algorithms.
B.8 ATTENTION-
REWARD
In the main text, we the evolution of visual attention throughout the reasoning process proxy to examine whether our method mitigates visual forgetting. Therefore, a natural baseline to consider more trivial alternative: directly using the attention scores of image tokens as a reward signal to guide training. To explore this possibility, we introduce an attention reward, which quantifies the model's overall visual attention ratio across layers during the reasoning process, as detailed in Appendix A.4 We replace the perception reward with this attention-based reward while keeping all other experimental configurations consistent with the main setup, termed as VAPOatn- whereas our original method using perception reward is referred to as VAPOpere.
As shown in Table IO naively maximizing visual attention provides little to no performance gain and even leads to noticeable degradation certain tasks. We hypothesize that this is due to two key factors: 1) Although visual attention can serve as an indicator of the contribution of visual inputs to the model's decision-making, it does not directly reflect whether the model is effectively utilizing visual features. Blindly encouraging higher attention may disrupt the base model's learned distribu- tion; 2) A higher visual attention ratio is not inherently better. As observed in earlier experiments, its values typically lie between 0) and 0.3. Unconstrained maximization of this ratio may lead to instability or training collapse in later stages.
B.9 COMPUTATIONAL EFFICENCY AND COST
In this section, we analyze the efficiency and computational cost of method, and compare it against other policy gradient algorithms in terms of training time and performance gains. We con- sider two representative reinforcement learning baselines: GRPO and DAPO YYu et al., 2025). The latter is a widely adopted variant of GRPO that incorporates several advanced techniques, including dynamic sampling and higher clipping ratio. As shown in Table [T] under the same data budget and training epochs, DAPO requires approximately 6 additional hours of training time compared GRPO, whereas our method incurs only a marginal increase of around 30 minutes. More im- portantly, this modest computational overhead yields substantial performance gain: our method improves accuracy by 4.37% over GRPO, significantly surpassing the benefits provided by DAPO.
This efficiency is largely attributable to the nature of our perceptual supervision. The key difference of our method from GRPO lies in the anchor scoring process, where the model is asked evaluate visual claims. Unlike standard rollouts, which require generating full reasoning traces, our method only requires binary judgment, iee,, yes or no for each claim, essentially single-token output. Consequently, this process is highly efficient. Moreover, identical to that GRPO. These results suggest that our approach introduces minimal additional computational cost while achieving notably greater performance improvements.
gradient update procedure remains
Beyond training, In VAPO, we leverage LLMs, typically GPT to generate visual claims for assess- ing the model's perception capability. Here for completeness, computational time required to generate these visual claims, based on our usage records. As shown in Table 12, the time is mainly spent querying GPT and the subsequent filtering process. The
provide below estimate of the
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Dataset
Total
Querying
Filtering
ViRL39K
38min
34min
4min
Table 12: The time costs for generating visual claims.
querying stage refers to the waiting time after submitting prompts, which also includes re-queries for generated claims that fail meet the specified requirements. The filtering stage applies set of rule-based criteria as described in Fig. [T]to remove claims that do not satisfy our standards, for ex- ample, those that are excessively long, contain uncertainty markers such as "maybe" or "appears", or violate the required balance between correct and incorrect claims. Besides, we also remove redun- dant claims that are semantically repetitive with embedding models, and limit the number of claims from various types, e.g., color, relational and OCR, through regular expressions. Any filtered-out examples are resubmitted until the target number of valid claims is reached. Overall, the entire pro- cess takes roughly half an hour, which is negligible compared with the training phase and remains highly efficient relative to methods such as DAPO even considering the overall pipeline costs. is important to note that this claim generation process is one-time procedure, and no further GPT querying is required for subsequent training runs.
B.10 LIMITATION ANALYSIS
Visual Claim Quality. One factor influencing the effectiveness of VAPO is the quality of the gener- ated visual claims. This includes whether the claims are strongly vision-dependent, clearly verifiable from the image, and correctly labeled. As such, the claim generation model, i.e., GPT-5 in our cur- rent setup, may become a limiting factor for overall performance. However, this also suggests that VAPO retains great potential for further improvement: leveraging higher-quality visual claims, ei- ther generated by more capable models annotated by human experts, could potentially enhance the effectiveness and further improve the model's performance ceiling.
Hyperparameter Sensitivity. Our method introduces several new hyperparameters including the anchor number K, the late-emphasis weight 3, and the perception reward weight y, all of which require careful tuning achieve optimal performance. Moreover, the optimal settings for these hyperparameters may vary across task types: vision-intensive tasks tend to benefit from larger values of B and y, whereas logic-heavy tasks such as mathematical reasoning often prefer smaller values. Designing an adaptive task characteristics remains an important direction for futuree work.
policy that dynamically adjusts these parameters based on
Single Image Setting. In the current work, we focus exclusively on single-image tasks for simplic- ity. However, VAPO can be readily extended to multi-image or even video- based tasks by generating visual claims that reference multiple frames or views. Exploring the benefits of vision-anchored training in such settings, particularly in multi-image reasoning or temporal video understanding presents an interesting and promising direction for future research.
B.11 CLAIM AND LABEL QUALITY
Upon obtaining the generated claims, to assess the quality and potential noise of both the visual claims and their corresponding labels, here we conduct straightforward yet reliable human evalu- ation as an indicator for their effectiveness prior to training. Specifically, we randomly sample 250 examples from the training set and evaluate each generated claim to determine whether it is visual- dependent, i.e., grounded in the visual cues of the image rather than deducible solely through textual reasoning, and whether it is answerable, i.e., has a clear and unambiguous ground-truth answer. For the labels, we manually verify the accuracy against the corresponding question and visual input. Below, we report the recorded human evaluation statistics for the claims and labels.
As shown in Table 13 the generated claims exhibit high overall quality according to human evalua- tion, where the noise in the claims occupy only around 2%, i.e., claims that are not visual-dependent not answerable, and the labeling error rate is below 4%. In addition, for comprehensiveness, here we also perform model evaluation, in which we sample 2000 examples and feed the generated claims and labels back to GPT and prompt it to act judge to assess noise and accuracy as defined
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Human Evaluation
Visual- -oennd-Dpennett... (%)
Answerable (%)
Accuracy (%)
Claims Labels
98.44
99.30
96.78
Table 13: The human audit of the generated claim and label quality.
Model Evaluation
Visual- Soeaal-Deendent (%)
Answerable (%)
Accuracy (%)
Claims
97.29
99.67
Labels
95.85
Table 14: The model evaluation of the generated claim and label quality.
above. The above results Table 14 indicate that model and human evaluation of claim quality are closely aligned, suggesting that the generated claims and labels reliable. As highlighted in our limitation analysis, further reducing the noise and error rates has the potential to yield additional performance gains and further enhance the effectiveness of our approach.
B.12 METHOО SENSITIVITY TO OTHER GENERATORS
In our main experiments, employ GPT-5 to generate visual claims due to its strong performance and cost-effectiveness. Here we further examine how using open-source, weaker models to gen- erate visual claims influences VAPO's effectiveness. Specifically, we consider two typical VLMs: LLaVA-1.6-34B (Liu et al.] (023) and Owen2.5-VL-322. (Bai et al. (025). For each model, we input the instructions directly
prompts and apply filtering mechanism similar to that used with GPT-5 to remove claims that do not meet our requirements. We train VAPO on a subset of 5000 examples randomly sampled from the full training set using the claims generated by these models, and evaluate results on the established benchmarks. As shown in Table [5] despite being open-source models, Qwen2.5-VL achieves performance comparable those obtained with GPT-5, yielding nearly identical average improvements over GRPO (+0.2%). Similarly, LLaVA-116, one of the pioneering VLMs, also attains nearly the full performance achieved with GPT-5 (+0.5%). These results indicate that VAPO does not have to rely on closed-source, source, state-of ofe----ftteerrrr models to be effective; smaller open-source models are capable of delivering almost the same performance gains.
B.13 VAPO ON OTHER MODELS
Following prior work (Huang et al. 2025 Wang et al. 2025a), we use Qwen2.5-V. VL as a standard base model to evaluate the effectiveness capabilities. We posit that visual forgetting is primarily driven by two factors: 1) Training data. The existing training corpora (e.g., mathematics text-intensive tasks) place heavy emphasis on textual patterns, leading models to develop strong text bias; 2) Training objective. The widespread use of next-token prediction objective encourages models to prioritize autoregressive consistency, causing attention to visual inputs to become progressively diluted as the output sequence grows, as shown Fig. of the main paper. Therefore, unless these underlying factors are substantially altered, we expect that visual forgetting will likely generalize across different model architectures.
method, given its strong and representative reasoning
To provide a preliminary validation of this assumption, here we additionally experiment with Qwen3-VL-SB (Yang et al. 2025a), more recent and more capable reasoning model whose
Model
Math Verse
LigicVista
Geometry3k
MMMU
MMStar
HallBench
Avg.
GRPO
44.2
44.4
44.5
53.6
55.2
51.2
49.0
LLaVA-1.6 1.6
48.1
46.7
47.0
56.5
58.6
55.0
52.0
Qwen2.5-VL
48.5
47.0
47.4
56.7
58.9
55.2
52.3
GPT-5
48.9
47.3
47.7
56.7
59.1
55.5
52.5
Table 15: The VAPO effectiveness given claims generated with other models.
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Model
Math Verse
Logie Vista
Geometry3k
MMMU
MMStar
HallBench
Avg.
Qwen3-VL
58.8
56.7
59.3
68.4
67.3
59.2
61.8
GRPO
63.5
60.1
64.4
72.7
68.3
60.4
64.9
VAPO
67.6
65.8
68.7
74.2
72.5
63.9
68.8
Table 16: The results of VAPO other base models.
Model
V*
RealWorldQA
Avg.
Qwen2.5-VI 71.4
64.3
67.9
Hi-OneVision
73.5
65.7
69.6
VI.AA-Thinker
74.8
66.3
70.6
Vision-R1 74.1
66.8
70.5
VAPO-Thinker
77.9
69.4
73.7
Table 17: The results of VAPO on more evaluation tasks.
chitecture differ substantially from those of Qwen2.5- VL. We apply VAPO to Qwen3-VL. to assess whether our method remains effective under different scenarios. As shown in Table 16 our method continues to provide substantial improvements over GRPO when applied to Qwen3-VLL -Ven.VVL, yielding an average gain of 3.9% (64.9% + 68.8%). This suggests that, beyond Qwen2.5-VLL other reasoning models may also suffer from pronounced visual forgetting, and that our method is broadly applicable and effective across different model architectures.
B.14 EVALUATION ON MORE TASKS
During training, our visual claims are designed to span multiple dimensions of perceptual capability, including fine-grained visual details, e.g., "the whiteboard says No Parking", and spatial reasoning, perception training enhances
e.g., "the bus is to the left of the motorcycle". Consequently, performance on tasks requiring these capabilities. For completeness, here we further evaluate our model
popular benchmarks, V* (Wu & Xiel (024) and RealWorl, which target fine- grained perception and spatial reasoning, respectively. As shown in Table [7 our model achieves an average improvement of 3.1% (70.6% + 73.7%) over the previous best results on both fine-grained and spatial reasoning benchmarks. This empirically demonstrates that VAPO generalizes well to broad range of inneppiion-intnsiin tasks.
B.15 VAPO WITH SELE-SCPERVISED ANCHORS
The core idea of VAPO is to utilize anchors to assess model's perception capability during reasoning process and use curated rewards to encourage stronger reliance on visual inputs. The specific form of the anchor (i.e., the mechanism used to measure perception), is not essentially limited to binary textual claims. We choose textual claims mainly because 1) efficiency: As discussed the effi- ciency analysis, current pipeline introduces only minimal training overhead, offering excellent cost-pent pessforrrore trade-off; 2) simplicity, unlike reconstruction. based methods which may require additional learnable decoders more complex architectures, textual claims allow us to probe per- ception capability with only single-token output; 3) effectiveness: Our experiments also demon- strate that textual claims significantly improve model accuracy, surpassing previous strong baselines.
When designing the visual anchors, we also considered reconstruction-bart anchors, where the model would be required reconstruct visual features or undergo linear probing assess its per-
Model
Math Verse
Logic Vista
Geometry3k
MMMU
MMStar
HallBench
Avg.
Base Model
40.7
42.6
38.5
52.7
54.9
50.0
46.6
GRPO
44.2
44.4
44.5
53.6
55.2
51.2
49.0
VAPO SupervSuper
48.1
46.5
48.3
55.9
57.9
55.1
51.9
VAPO (GPT)
48.9
47.3
47.7
56.7
59.1
55.5
52.5
Table 18: The results of VAPO with self-supervised anchors.
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Cut Position (%)
20
40
60
80
Agg-
Recoverable Ratio (%)
4.5
5.1
8.8
7.6
15.4
Table 19: The single-cut recoverable ratio by early decision.
ceptual capability. However, as mentioned above, these approaches introduce additional learnable parameters, thereby increasing computational and training complexity.
Another promising self-supervised direction is to adopt bootstrapping approach, in which the model generates its own visual claims and subsequently uses them as training signals, thereby elim- inating the need for external models. Here we explore whether this more efficient training strategy for VAPO remains effective. Specifically, instead of relying on GPT to produce visual claims, we allow the model being trained to generate its visual claims during the rollout step using the pro- vided instructions, and then use these self-generated claims to assess its perceptual capability and update the model accordingly. This procedure is self-supervised and iterative: the model's gener- ated claims help improve its visual capability, which turn further refines the quality of subsequent claims. We use Qwen2.5-VL-7B training set. As shown in Table 18 this self-supervised approach retains the vast majority of the effectiveness observed in the default (GPT) setting, with only a modest average difference of 0.7% (46.5% + 47..%). This indicates that, even without relying on any external or stronger models, VAPO can still achieve highly promising performance.
the base model and train 5000 example subset of the full
B.16 SINGLE-CUT RECOVERABLE RATIO
Our current definition of the recoverable ratio uses a relatively loose metric intended to estimate the proportion of eтror cases influenced by the reasoning process, serving as an oracle-style indicator of the potential for our proposed method. Here, we additionally report recoverable ratios at spe- cific single-cut positions.
particular, we provide the recoverable ratios measured four relative reasoning depths (20%, 40%, 60%, and 80% of the reasoning length) well as their aggregated result. shown in Table [9 even when considering only single reasoning position, the model's recoverable ratio remains substantial, reaching nearly 10% the 60% and 80% positions. This in- dicates considerable headroom for improving the accuracy of current models and helps explain why observe
excessively long reasoning degrade performance, observed in Fig. 2 that later positions exhibit higher recoverable ratios, suggesting that the latter stages reasoning have a particularly strong impact the model's 's reliance visual information, consistent with the visual forgetting phenomenon shown in Fig. 3
Moreover,
B.17 DATA DEDUPLICATION CHECK
In our training setup, follow prior work (Wang et al. 2025a d) by training on ViRL39K and eval- uating on both mathematical benchmarks (e.g.. Vista and seneral-purpose benchmarks (e.g., MMStar). Here perform data deduplication checks between the training set and the evaluation benchmarks. Specifically, we compare image hash values across datasets and compute the number of overlapping instances. As shown in Table 20 both the mathematical benchmarks (6 in total) and the
benchmarks (4 in total) exhibit 0% overlap with the training dataset based on image-hash comparison. This indicates that no duplicate samples exist between the training and evaluation sets, and thus there is no risk of data contamination.
Deduplication Check
Mathematical
General
Total
Overlap Ratio (%)
0
0
Table 20: The overlap ratio between training and evaluation set.
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Training Steps
0
25
50
75
100
Activation Rate (Easy)
0.53
0.65
0.72
0.76
0.79
Activation Rate (Hard)
0.21
0.44
0.56
0.61
0.64
Table 21: The accuracy gate activation gate across easy and hard examples.
Method
MathWerse
Logic Vista
Geometry3k
MMMU
MMStar
HallBench
Avg.
GRPO
44.2
44.4
44.5
53.6
55.2
51.2
49.0
VAPO (strict gate)
48.9
47.3
47.7
56.7
59.
55.5
52.5
VAPO (relaxed gate)
48.5
47.4
47.2
56.3
59.4
55.0
52.3
Table 22: The results of the curriculum gating strategy.
B.18 EFFECT OF ACCURACY GATE
In training, incorporate accuracy gate to prevent reward hacking, where the model might pursue perception rewards at the expense of task accuracy by producing trivially short reasoning. Although the accuracy gate is triggered more frequently on easy problems, resulting in a larger portion of trajectories receiving perception rewards, these rewards are ultimately normalized when converted into advantages. This normalization step ensures that the generally higher rewards ob- tained from easy examples do not disproportionately influence the final advantage. In contrast, hard problems, normalization scales up the advantages of correct trajectories, encouraging the model to pay additional attention to these challenging cases. Therefore, the accuracy gate does not bias training toward simpler examples.
To further analyze how the accuracy gate influences the training dynamics of easy and hard prob- lems, here we report the accuracy gate activation rates over the course of training for both categories. We classify problems easy hard based the model's initial accuracy gate activation rate (> 0.4 for easy and < 0.4 for hard) and track how these activation rates evolve at different training steps. We conduct training activation rate increases substantially for both easy and hard questions, and the gap between the two progressively narrows. This further indicates that the model does not over or under-optimize either category during training, suggesting balanced learning dynamics across problem difficulties.
5000 example subset of the full training set. As shown in Table 21 the
Moreover, to investigate whether relaxed accuracy gate can further improve performance, we ex- periment with curriculum gating strategy. Specifically, unlike our default setting, which applies strict binary gate, we gradually loosen this constraint over the course of training. That is, we progressively assign an increasing proportion of the perception reward to incorrect trajectories and eventually remove the accuracy gate entirely by granting full perception rewards. As before, we conduct this study using 5000 example subset of the full training set. As shown in Table [2 us- ing a relaxed accuracy gate does not yicld noticeable performance improvements. This aligns with our earlier analysis that the default accuracy gate does not under-optimize hard examples, and that relaxing the gate introduces a greater risk of reward hacking.
B.19 EFFECT OF ANCHOR PLACEMENT STRATEGY
Our current anchor placement strategy resembles the early decision scheme, where anchors are inserted at semantic boundaries (c.g., commas, periods, line breaks) randomly throughout the entire reasoning trajectory. In designing this component, we also considered several alternative placement strategies: 1) uniform, where anchors are inserted at fixed intervals rather than randomly; 2) back- loaded, where anchors are inserted only in the latter 60% of the reasoning process, acting as a targeted mechanism to address severe visual forgetting that tends to occur toward the end of long trajectories; 3) front-loaded, where anchors are inserted only within the first 60% of the trajectory, serving as baseline comparison. We ensure that all three variants insert the same number of anchors (K = 20). We train each variant on 5000 example subset of the full training set, and we report the results of these placement strategies.
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Position
Math Verse
Logic
Geometry3k
MMMU
MMStar
HallBench
Avg.
GRPO
44.2
44.4
44.5
53.6
55.2
51.2
49.0
Uniform
48.4
47.1
47.5
56.1
58.4
55.0
52.1
Front-loaded
48.1
46.7
47.0
55.9
58.5
54.4
51.8
Back-loaded
48.7
46.9
47.7
56.2
59.0
55.8
52.4
Random
48.9
47.3
47.7
56.7
59.1
55.5
52.5
Table 23: The effect of varying anchor placement strategies.
Method
Math Verse
VisicVista
Geometry3k
MMMU
MMStar
HallBench
Avg.
Base Model
40.7
42.6
38.5
52.7
54.9
50.0
46.6
MM-Eureka
50.3
48.5
50.2
55.7
60.4
54.8
53.3
PAPO
50.4
47.3
49.7
60.8
61.8
55.6
54.2
TL-Rethinker
54.2
47.2
49.6
57.9
61.8
55.4
54.4
VAPO
53.3
50.9
51.3
60.2
63.0
57.4
56.0
Table 24: The comparison between VAPO with more baselines.
As shown in Table 23 the simple naive (random) strategy performs better than other placement variants. We attribute this to several factors: 1) uniform insertion, which places anchors at fixed intervals, is pendence only at predetermined positions; 2) front-loaded insertion performs noticeably worse than both random and back-loaded insertion, indicating that later positions the reasoning trajectory are more critical for improving perceptual grounding; 3) In addition, although back-loaded insertion is slightly weaker than random insertion on average, achieves comparable or even superior perfor- mance on MMStar and HallusionBench. This suggests that vision-heavy benchmarks may benefit from a larger number of anchors or greater weighting at later reasoning steps, which aligns with our late-emphasis design.
susceptible reward hacking, the model may learn to exhibit high visual de-
B.20
COMPARISON WITH MORE BASELINES
Here, for completeness, we compare our approach with more baselines, including PAPO (Wang et al., 2025d), MM-Eureka (Meng et al. 2025) and VL-Rethinker (Wang et al. 2025a). We use the publicly released 7B checkpoints from their repositories. For PAPO, we reproduce the results using greedy decoding, their paper reports performance using the avg @8 accuracy metric, which is not directly comparable to the metrics adopted in prior work (Wang et al. 2025al Huang et al.) 2025). As shown in Table 24 VAPO achieves an average improvement of 1.6% over the previous best results (54.4% + 56.0%) further demonstrating the effectiveness of our approach.
B.21 EFFECT OF VAPO ON LONG-THOUGHT EXAMPLES
Our ablation study in Fig. shows that increasing K, i.e., using more anchors, generally yields pos- itive gains across examples. To further examine whether longer-thought examples benefit dispro- portionately from larger K, we partition benchmark examples into two groups based on the output length of our VAPO-Thinker-777 model: short-thought (< 300 tokens) and long-thought (> 300 tokens). We then evaluate models trained with different values K and analyze how performance trends vary across these categories. As shown in Table 25 increasing K leads to stable perfor- mance improvements for both short- and long- thought examples, and the magnitude of these gains
Length
1
5
10
15
20
Short
53.7
56.0
57.5
58.4
58.5
Long
47.3
49.4
50.9
51.5
51.8
Total
51.5
53.8
55.1
55.8
55.9
Table 25: The results of VAPO on short and long-thought examples.
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Models
Math Verse
Logic Vista
Goometry3k
MMMU
MMStar
HallBench
Avg.
RI-One
299
308
287
263
232
201
265
Vision-RI
337
293
341
283
239
251
291
315
287
334
275
254
238
284
Table 26: The average reasoning length of VAPO,
Method
BLINK
MuirBench
Avg.
Base model
55.3
58.1
56.7
GRPO
57.0
60.4
58.7
VAPO
60.1
62.2
61.2
Table 27: The results of VAPO multi-image tasks.
remains largely consistent across the two groups. This trend is also aligned with the ablation results reported in Fig. 7
B.22 REASONING LENGTH DISTRIBUTION
The goal of VAPO is to enhance the effectiveness of reasoning by improving the model's perceptual capability, rather than alter the length of its reasoning traces. Accordingly, our pipeline does not incorporate any explicit length regularization. Here we report the average reasoning length of our models compared with other baselines across the established benchmarks. As shown in Table 26 the average output length of our model does not differ noticeably from that of other baselines. This rules out the possibility that our performance gains stem merely from longer reasoning traces, and instead indicates that VAPO improves the effectiveness of the reasoning process itself.
B.23 VAPO ON MULTI-IMAGE TASK
Our current visual claims are primarily designed for single-image inputs, which is sufficient vali- date the core motivation of VAPO, namely improving the model's 's reliance on visual information and mitigating visual forgetting. Here we further conduct a small-scale pilot study on multi mulli--imaee tasks. Specifically, we train VAPO exclusively on the multi-iman subset of ViRL39K, which contains ap- proximately 2,500 examples. For claim generation, we consider two types of claims. The first type is single-image claims, which mirror original setup and are generated with respect to only one image in the multi-image set. The second type cross-image claims, which capture relationships or distinctions across images. For the former, we provide GPT with only one image at time. For the latter, we present all images simultancously and extend the original instructions with an additional requirement that the generated claim must reference information across images (e.g., "the two im- ages depict the same building"). We ensure that each training example contains a balanced number of claims from both categories. The training procedure remains identical to main pipeline, and GRPO is used as the baseline for comparison. We evaluate the trained models on two representative multi-image benchmarks, including BLINK (Fu et al. 2024) and MuirBench (Wang al. 2024a).
As shown in Table 27 despite being trained on a limited amount of data, our method still achieves an average improvement of 2.5% over GRPO on mulei-image tasks (58.7% 61.2%). This demon- strates that our approach is not restricted to the single-image setting and can generalize to multiple images as well. We leave the evaluation of video-based tasks as promising future work.
C. BROADER SOCIETAL IMPACTS
Our work carries several positive societal implications. Our findings reveal that existing VLMs of- ten produce reasoning that is not visually grounded, which can degrade performance and reliability. Such ungrounded reasoning may introduce misleading hallucinated content, thereby posing risks to model safety and trustworthiness. Moreover, since these reasoning traces often rely heavily on statistical patterns learned from training data, they may inadvertently expose sensitive information,
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raising concerns about data privacy and potential leakage. In contrast, the proposed VAPO frame- work steers the model's reasoning process to remain anchored in visual evidence. This grounding mechanism helps ensure that model outputs more faithful to the input image and less likely to include irrelevant or speculative content, thus promoting both factual accuracy and privacy preser- vation. At present, we have not identified negative societal impacts associated with this work. How- ever, due to external factors such as the availability of datasets and baseline implementations, further assessment may be necessary in the future.
D. SUPPLEMENTARY RESULTS
D.1 FULL NUMERICAL MAIN RESULTS
Due to space limitations in the main text, some baseline results are omitted. Here, we provide the full numerical results our method and all baselines across the ten established benchmarks Table 28 corresponding to Table [ and Table 2
D.2
NUMERICAL RESULTS OF K
Here we report the numerical results of the ablation study on the anchor number K in Table 29 corresponding to the visualization in Fig. (A).
D.3
NUMERICAL RESULTS OF
Similarly, we present the numerical results of the ablation study on the impact of B in Table 30 corresponding to Fig. (B).
D.4 FULL RESULTS OF AUGMENTED BASELINES
Here provide the full numerical results of method compared with baselines augmented with inference-level remedies, i.e., visual replay and focus prompt, in Table 31 corresponding to Table 3 in the main paper.
D.5 FULL RESULTS OF 3B PARAMETER SCALE
Here for completeness, report the evaluation results of our proposed method and baseline models at the 3B scale. To ensure fairness, we select baselines with comparable model sizes and exclude those without publicly available scale checkpoints. The results presented in Table 32
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Models
VathVerse Mathiisst MathVissoo Lisic WeMath Geo3k MMMU MMStar HallBeuch MMVer Avg.
Close-source Models
GPT:S-Thinking
Gemini-2.5-44
76.9
80.9
69.1
73.8
78.0
77.2
74.7
73.6
64.1
83.3
75.2
Open mwen-source Models
Qwen2.5-V1.
Int5mnV22.588B
34,5
68.2
25.6
38.3
38.6
44.8
56.2
63.2
49.0
62.8
48.1
R1-OmeVision-PP 7B
64.1
29.9
45.6
44.6
46.1
54.3
54.1
52.5
65.2
50.3
VLAA-Thinker-7B
68.0
26.4
48.5
41.5
50.6
49.7
54.7
70.0
51.7
Vision-R1-7B
52.4
73.5
28.2
49.7
41.6
49.0
$7.6
61.4
49.5
71.1
53.4
Our Modely
VAFO-Thinker-3B
35.8
67.1
23.9
39.7
35.4
44.2
55.6
59.4
49.5
64.6
47.5
VAFO-Thinker-128
53.3
75.6
31.9
50.9
43.6
51.3
60.2
63.0
57.4
71.9
55.9
Table 28:
The full numerical results of main experiments, corresponding to Table I and Table 2
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K Vathe Vasta Vathonsion
Logic
WeMath Geošk
MMMU
MMStar HallBench MMVet Avg.
48.2
70.6
26.1
45.5
39.1
47.3
56.6
58.9
$3.2
69.9
$1.5
5 50.9
73.1
28.4
47.8
41.2
50.5
58.4
61.3
55.6
70.4
53.8
10
52.8
74.8
30.7
49.5
42.5
51.2
59.9
62.2
56.7
71.2
55.1
31.5
50.4
51.0
60.4
62.7
57.0
71.6
55.8
20
53.3
75.6
31.9
50.9
43.6
51.3
60.2
63.0
57.4
71.9
55.9
Table 29: The numerical results of ablation study of K, corresponding to Fig. (A).
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8
Verse Vitt
Mathonsion Visia
WeMath Geo3k
MMMU
MMStar HallBench MMVet Avg
0.0
51.7
73.8
28.4
48.6
42.0
49,9
58.9
60.2
53.8
70.3
53.8
0.5
52.7
74.8
30.2
49.6
42.8
50.7
59.5
62.0
55.4
71.3
54.9
1.0
53.1
75.7
31.5
51.3
43,4
51.4
60.0
62.5
56.8
72.0
55.8
1.5
53.3
75.6
31,9
50.9
43.6
63.0
57.4
71.9
55.9
2.0
53.0
75.5
31.1
50.5
43.1
51.5
56.6
71.6
55.6
2.5
51.8
74.8
30.4
50.0
42.6
50.5
59.8
62.2
55.3
70.2
54.7
Table 30: The numerical results of ablation study of B, corresponding Fig. (B).
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Models
Verse
MathVista Vishnn
LogicVista
WeMath Gea3k
MMMU
MMStar
HallBench
MMVet
VLAA-Thinker-7B
48.2
68.0
26.4
48.5
41.5
50.6
59.1
49.7
54.7
70.0
51.7
Freus Prompt
48.8
68.4
27.3
49.2
41.8
50.7
59.7
51.1
55.2
70.7
52.3
Visual Replay
69.8
42.3
60.5
$2.9
71.7
Vision R1-7B
52.4
73.5
28.2
49.7
41.6
49.0
$7.6
61.4
49.5
71.1
53.4
Prompi
Visual Replay
$3.4
75.2
29.9
50.7
42.5
50.5
59.4
62.1
51.8
71.9
54.7
VAPO-Thinker-2
53.3
75.6
31.9
50.9
43.6
51.3
60.2
63.0
57.4
71.9
55.9
Table 31: The full results of baselines augmented with test-time remedies, corresponding to Table
37

---

Published as a conference paper at ICLR 2026
Models
MathVerse MathVisst MathVision Visic
WeMath Geo3k MMMU
MMStar HallBeuch MMVer Avg.
Qwen2,5-VI. V1.-3B
30,5
60.3
19.8
37.6
20.4
31.7
48.6
51.5
44.0
58.4
40.3
1..2.2V1...522
36.1
42.3
62.6
36.1
RI-OneVision-SB
35,5
23.7
VLAA Thin-hhinee-331 3B
61.0
24.4
38.5
33.8
42.7
50.5
56.2
45.1
63.9
45.3
Thinker--B
38.2
64.5
27.3
41.4
35.3
44.2
51.6
59.4
49.5
64.6
47.6
Table 32: The results of 3B model and baselines of comparable scale. " indicates unavailable results due to unreleased checkpoints. Vision-R1 is excluded as it does not consider 3B scale.
38


Reviews:
[{"review_id": "C1gGVnCF8q", "reviewer": "Reviewer_YzHK", "summary": "This paper identifies a dual nature of reasoning in VLMs: while extended reasoning enhances logical capability, it simultaneously weakens visual perception. The authors provide empirical evidence across multiple reasoning benchmarks showing that early-stage reasoning improves accuracy, but performance declines in later stages, with most errors arising from visual perception failures. To address this, the paper first introduces an early-decision mode, where the model is prompted to terminate reasoning mid-trajectory, which mitigates visual forgetting and yields performance gains. Visual Replay and Focus Prompt are also designed to increase attention to visual tokens. Finally, the paper proposes VAPO, a simple yet effective policy-gradient algorithm that inserts visual anchors along the reasoning trajectory to encourage consistent visual perception. Experimental results demonstrate that the proposed VAPO-Thinker-7B improves the model's reliance on visual input and achieves average gains of 2-4% over strong baselines on established benchmarks.", "questions": "- I am interested in the error distribution of the trained VAPO-Thinker. What is the current proportion of perception errors, and do recoverable errors still exist?\n\n- Did the authors verify the reliability of GPT-5–based annotation and claim generation? How was the quality of these generated annotations/claims evaluated?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper is clearly written and easy to follow.\n\n- The experimental setup is sound and convincingly supports the core claim that prolonged reasoning leads models to neglect visual cues and commit perception-related errors. The quantitative error analysis is comprehensive, showing that perception errors dominate.\n\n- The training framework is effective and well-motivated. I like the design of visual anchors and the perception reward.", "weaknesses": "- Since both the insertion of visual anchors and the sampling of reasoning prefixes are random, some anchors may appear in contexts that are unrelated to the visual content described by the corresponding claim, or lack sufficient logical cues for the model to judge the correctness.\n\n- The generation of visual claims relies on GPT-5, which may introduce noise or bias in their quality. This has been pointed out in their limitations.", "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": "0ODHawCz15", "reviewer": "Reviewer_4nqT", "summary": "This paper investigates a key contradiction in Vision-Language Models (VLMs), termed the “dual nature” problem. While Chain-of-Thought (CoT) reasoning improves logical inference, excessively long reasoning chains can impair visual grounding, leading to errors on simple visual tasks. The authors attribute this to “visual forgetting,” where attention to the image fades as reasoning progresses.\nTo validate this, the paper introduces two inference-time interventions:\n1. Visual Replay – reintroducing the image mid-reasoning.\n2. Focus Prompt – instructing the model via text to re-attend to the image.\n Both methods mitigate degradation, supporting the visual forgetting hypothesis.\nTo address the issue fundamentally, the authors propose VISION-ANCHORED POLICY OPTIMIZATION (VAPO), an RL-based algorithm that inserts visual anchors (short, image-related statements) into reasoning paths. The model judges their correctness and receives a perception reward, encouraging sustained visual grounding.", "questions": "1. Sensitivity to Claim Quality: How does performance change if visual claims are generated by weaker models (e.g., LLaVA-1.6, Qwen-VL-Max)? Are there filtering mechanisms?\n2. Universality of Visual Forgetting: This study experiments with models based on Qwen2.5-VL. Do you believe the \"visual forgetting\" phenomenon is prevalent across all types of VLM architectures? \n3. Generalization of VAPO: Does the perception training generalize to fine-grained or spatial reasoning tasks beyond truth verification?\n4. Alternative Anchors:  Besides using GPT-5 to generate explicit text claims, have you considered other forms of \"anchors\"? For example, could you explore self-supervised or reconstruction-based anchors to reduce dependence on external models?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Important and Novel Problem: Identifies a counterintuitive phenomenon—“more reasoning can reduce accuracy.” This challenges a major assumption in VLM research.\n2. Strong Empirical Validation: The “visual forgetting” hypothesis is convincingly supported through attention visualizations and well-designed interventions (Visual Replay, Focus Prompt).\n3. Elegant Method Design: VAPO elegantly integrates perception evaluation into RL training through visual anchors and perception rewards, directly addressing the root cause.\n4. Comprehensive Evaluation:\n  - Covers 10 benchmarks across reasoning and VQA tasks.\n  - Compares against both closed- and open-source SOTA models.\n  - Provides detailed analyses and ablations confirming the method’s robustness.", "weaknesses": "1. Reproducibility: Reliance on a closed-source, super-capable large model (GPT-5) may create difficulties for other researchers trying to reproduce or extend this work.     \n2. Cost and Efficiency: Generating a large number of visual claims for the training data introduces considerable computational overhead.    \n3.  Quality Bottleneck: The upper bound of VAPO's effectiveness may be limited by the quality of the claim generation model. Low-quality claims could negatively impact training.\n4. Added Complexity: Introduces additional steps, hyperparameters (K, β, γ), and tuning difficulty compared to standard GRPO.\n5. Efficiency Discussion Limited: Although runtime overhead is small (Table 10), total pipeline cost—including data generation—remains higher than baseline.", "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": "x57fQ8UtUx", "reviewer": "Reviewer_KfZR", "summary": "This paper investigates a \"dual nature\" of reasoning in Vision-Language Models (VLMs), claiming that while reasoning enhances logical inference, it can impair perceptual grounding. The authors attribute this phenomenon to \"visual forgetting,\" where prolonged reasoning causes the model to disregard visual input. They propose VISION-ANCHORED POLICY OPTIMIZATION (VAPO), an RL-based method with a \"perception reward\" to force the model to maintain visual grounding", "questions": "1. On the Confounding Variable: Did the authors run any control experiments to disentangle the act of reasoning from the length of the textual context? For example, what happens to perceptual accuracy if the model is fed a long context of another question-solution or a few other images ?\n2. On the Experimental Design: The \"visual replay\" intervention (Fig. 3) simply re-inserts the image. This intervention successfully alleviates the performance drop. Does this not strongly suggest that the problem is a standard attention failure (i.e., the model \"forgot\" the image in its context) rather than a more complex cognitive \"harm\" caused by the reasoning process itself?\n3. On the Method's Motivation: Given the lack of evidence for a true \"reasoning vs. perception\" conflict, isn't VAPO more accurately described as a powerful regularization technique to combat visual context drifting? Why frame it as solving a cognitive duality when the evidence only points to a context length/attention failure?\n4. On the Error Analysis: In Fig. 2, the paper claims reasoning induces perception errors. Could the causality be reversed? That is, could the model make an early logical error (e.g., path 1 in Fig 1, deciding 7.4+7.2+8.1=22.7 instead of 20.5) and then hallucinate visual \"evidence\" to justify its flawed reasoning path, rather than the reasoning itself degrading its ability to see?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 1, "presentation": 2, "contribution": 2, "strengths": "1. The \"early decision\" mode of analysis (Fig. 2A) is a clever and effective method for demonstrating this phenomenon of the degradation of perceptual accuracy as the generation process continues. \n\n2. VAPO showcase an effective solution of rewarding the model for maintaining perceptual grounding,", "weaknesses": "The primary weakness of this paper lies in its central conceptual claim. The argument that \"reasoning harms perception\" is a significant overstatement that is neither logically sound nor intuitively convincing.\n\n1. **Flawed Causal Claim (Logical):** The evidence presented demonstrates a **correlation between reasoning length and perceptual decline, but it fails to establish causation.** The \"prolonged reasoning\" is perfectly confounded with the length of the generated text context. The paper provides no evidence to disprove the more parsimonious hypothesis: any sufficiently long context (it could be textual reasoning or it could be many image tokens) would cause the model's attention to \"drift\" from the visual input, leading to perceptual errors. The paper mistakes correlation for causation.\n\n2. **Flawed Premise (Intuitive):** The claim is also nonsensical from a cognitive perspective. In human intelligence, perception and reasoning are synergistic, not conflicting. Perception provides the \"ground truth\" (premises) upon which reasoning operates. Reasoning, in turn, can direct perceptual focus (e.g., \"look for the red car\"). Claiming an inherent conflict in AI models based on this confounded evidence is an extraordinary claim that would require extraordinary proof, which the paper does not provide. It is far more likely that the model is simply failing at a known, mundane problem (long-context attention drift) than it has developed a special cognitive conflict different from how human visual system and brain work. \n\nThe main experiment results do not convincingly show the significance of perceptual grounding, without comparing with state-of-the-art RL approaches on VLM that do not explicitly facilitate perceptual grounding, such as MM-Eureka and VL-Rethinker. \n\nSince the central argument is flawed and misleading, my true rating for this paper is 3. From my perspective, the current version is not ready for publication.", "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": "PMfA9xVMbR", "reviewer": "Reviewer_j11z", "summary": "The paper investigates the dual nature of multimodal reasoning in vision-language models, showing that while early reasoning often improves performance, prolonged reasoning can weaken perceptual grounding and reduce accuracy on visually simple questions. Using an “early decision” protocol that forces answers at intermediate points, the authors find that accuracy first increases and then declines as reasoning continues. Error analysis shows perception errors dominate and many could be avoided by stopping earlier. This phenomenon is attributed to visual forgetting, where attention to visual tokens decays over time. Two inference-time fixes—visual replay and focus prompts—temporarily restore visual attention and improve accuracy. To address the issue during training, the authors propose VAPO (Vision-Anchored Policy Optimization), which inserts visual claim anchors along the reasoning process and introduces a perception reward that encourages correct yes/no judgments of image-grounded claims. With late-emphasis weighting and an accuracy-gated reward, VAPO-Thinker-7B achieves state-of-the-art results on multiple benchmarks, particularly those requiring strong visual grounding, while maintaining efficiency. Ablations confirm the effectiveness of more anchors, later-anchor emphasis, stable training dynamics, and superiority over attention-only rewards and inference-time fixes.", "questions": "- Claim quality and robustness:\n  - What fraction of GPT-5 claims were mislabeled upon manual audit? Can you report inter-annotator agreement or a small-scale human validation of claim correctness and vision-dependency (Sec. A.6; Table 6)?\n  - How sensitive is VAPO to weaker generators? Please provide results with an open-source vision-capable LLM to generate claims and quantify the drop from GPT-5.\n  - Do you filter claims for redundancy or triviality (e.g., color naming in synthetic diagrams)? Any diversity constraints?\n- Reward design:\n  - How often does the accuracy gate mask perception reward during training? A histogram versus steps would reveal whether hard items are under-optimized.\n  - Could curriculum gating (e.g., gradually reducing the accuracy condition) further improve performance on harder instances?\n  - Have you tried per-claim calibrated rewards (e.g., confidence-weighted yes/no) or multi-class verification to reduce sparsity?\n- Anchor placement:\n  - Beyond late-emphasis β, did you compare placement by semantic boundaries (sentence/step ends) versus uniform random? Any gains on MMStar/HallusionBench?\n  - How does K interact with sequence length at test time? Do longer-thought samples benefit disproportionately from more anchors?\n- Evaluation rigor:\n  - Please provide explicit data deduplication checks between ViRL39K and each benchmark, including image hash and near-duplicate detection.\n  - The “recoverable” metric (Fig. 2B–C) aggregates over multiple early stops. Can you report accuracy when stopping at a single, pre-specified percentile for all samples (e.g., 40% of tokens), to avoid optimistic selection?\n  - Reasoning length distribution: does VAPO change average reasoning length versus baselines? If so, how does that correlate with attention and accuracy?\n- Comparisons and scope:\n  - Clarify distinctions from perception-aware policy optimization (Wang et al., 2025c): is the novelty primarily the claim-anchored verification and late-weighting? Could their reward be used as an auxiliary alongside anchors?\n  - Any results on multi-image or video reasoning using multi-frame claims (future Sec. B.10 suggests feasibility)? A small-scale pilot would be informative.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "- Original analysis of multimodal reasoning dynamics: the early-decision protocol provides a fine-grained view of how different reasoning segments contribute to final accuracy, revealing a non-monotonic curve where longer reasoning can hurt (Sec. 3.1, Fig. 2A) and that the harm is largely perceptual (Fig. 2B–C).\n- Clear causal story with measurements and interventions: the visual-attention decay analysis (Fig. 3A) and simple test-time remedies (visual replay, focus prompt) that immediately spike attention and improve accuracy (Fig. 3B) support the visual forgetting hypothesis (Sec. 3.2).\n- Methodological novelty and simplicity: VAPO’s claim-anchored perception reward operationalizes “visual grounding” during RL without architectural changes. The late-emphasis weighting targets the observed late-stage forgetting (Sec. 4.2, Eq. 3–4).\n- Strong empirical results and breadth: consistent gains across 10 benchmarks with notable improvements on vision-intensive datasets (MMStar, HallusionBench) and competitive math performance (Tables 1–2, 11). Thorough ablations on anchor number K, weighting β, reward weight γ, KL coefficient, and baselines with test-time remedies (Fig. 7; Tables 3, 4, 7–9, 14).\n- Efficiency: small training-time overhead over GRPO and larger accuracy gains than a stronger RL baseline (DAPO) (Table 10).\n- Analysis depth: learning curves (Fig. 6), attention trends versus baseline (Fig. 5A), accuracy versus reasoning progress (Fig. 5B), and anchor-score diagnostics (Fig. 13) collectively strengthen the argument.", "weaknesses": "- Dependence on a closed-source claim generator (GPT-5) and limited auditing of claim correctness and vision-dependency. While Appendix A.6 provides prompts and Table 6 explores variants, there is no quantitative estimate of claim label noise or a human audit; method sensitivity to generator quality is acknowledged but not systematically evaluated.\n- The “recoverable by early decision” metric aggregates across multiple potential cut points (Fig. 2 caption) and may overestimate recoverability. A stricter, single-cut metric or randomized stopping analysis would better calibrate the effect size.\n- Potential overlap and contamination risks are not fully ruled out: ViRL39K composition (Sec. 5) merges several sources; explicit deduplication against evaluation sets is not reported. This is especially relevant for MMStar and MathVista where many public pipelines exist.\n- The accuracy-gated perception reward can bias learning toward easier instances where final answers are already correct, potentially under-incentivizing perception on hard items. An ablation that relaxes the gate or uses shaped rewards conditioned on partial correctness would clarify trade-offs.\n- Anchor placement strategy is mostly random with late weighting; more analysis of position selection (e.g., semantic segment boundaries versus uniform) would strengthen claims about targeting late-stage forgetting beyond the β ablation (Fig. 7B).\n- Comparison to closely related RL formulations emphasizing perception (e.g., perception-aware policy optimization [Wang et al., 2025c]) could be deepened to delineate conceptual/implementation differences and when claim-anchoring is preferable.\n- The attention metric is an indirect proxy; while useful, further triangulation (e.g., causal masking of image tokens at anchor points, feature attribution) would reduce the risk of over-interpreting attention as reliance.\n- Some related works about RL+Reasoning in VLM are missed. \n[1] Wang, Jiaqi, et al. \"Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models.\" arXiv preprint arXiv:2505.16854 (2025).", "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)