Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
Authors: Wenxuan Huang
Bohan Jia
Shaosheng Cao
Zheyu Ye
Fei zhao
Zhe Xu
Yao Hu
Shaohui Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal Large Language Models
Reasoning
Primary area: foundation or frontier models, including LLMs

Abstract: DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). 
Inspired by this breakthrough, we explore how RL can be utilized to enhance the reasoning capability of MLLMs. However, direct training with RL struggles to activate complex reasoning capabilities such as questioning and reflection in MLLMs, due to the absence of substantial high-quality multimodal reasoning data.
To address this issue, we propose the reasoning MLLM, Vision-R1, to improve multimodal reasoning capability. 
Specifically, we first construct a high-quality multimodal CoT dataset without human annotations by leveraging an existing MLLM and DeepSeek-R1 through modality bridging and data filtering to obtain a 200K multimodal CoT dataset, Vision-R1-cold dataset. It serves as cold-start initialization data for Vision-R1.
To mitigate the optimization challenges caused by overthinking after cold start, we propose Progressive Thinking Suppression Training (PTST) strategy and employ Group Relative Policy Optimization (GRPO) with the hard formatting result reward function to gradually refine the model's ability to learn correct and complex reasoning processes on the multimodal math dataset. 
Comprehensive experiments show our model achieves an average improvement of $\sim$6\% across various multimodal math reasoning benchmarks using only a 10K multimodal math data during RL training. 
Vision-R1-7B achieves a 73.5\% accuracy on the widely used MathVista benchmark, which is only 0.4\% lower than the leading reasoning model, OpenAI O1.
Scaling up the amount of multimodal math data in the RL training, Vision-R1-32B and Vison-R1-72B achieves 76.4\% and 78.2\% MathVista benchmark scores, respectively.
The datasets, weight and code will be released in: https://github.com/Osilly/Vision-R1.

Published as a conference paper at ICLR 2026
VISION-R1: INCENTIVIZING REASONING CAPABILITY IN MULTIMODAL LARGE LANGUAGE MODELS
Wenxuan Huang!.20
Bohan Jia
Zijie Zhai
Shaosheng Cao
Zheyu Yea
Fei Zhao" Zhe Xu
Yao Hu
Shaohui Lin 1,46a
1 East China Normal University
The Chinese University of Hong Kong 3Xiaohongshu Inc.
$ Key Laboratory of Advanced Theory and Application in Statisties and Data Science MMOE, China
(aarraanngg6@@@gmmaaiamm com (Wenxuan Huang)
shaohuilim gma 1. com (Shaohui Lin) Equal Contribution
t: Project Leader
[S: Corresponding Author
ABSTRACT
DeepSeck-RI-Ze has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore how RL can be utilized enhance the reasoning capability of MLLMs. However, direct training with RL struggles to activate complex reasoning capabilities such as questioning and reflection MLLMs, due to the absence of substantial high-quality multimodal reasoning data. To address this issue, we propose the reasoning MIL.M, Vision-RI, RI, to improve multimodal reasoning capabiliit. Specifically, we first construct high-quali multimodal CoT dataset without human annotations by leveraging an existing MLLM and DeepSeek- RI through modality bridging and data filtering to obtain 200K multimodal CoT dataset, Vision-RI-col dataset. serves as cold-start initialization data for Vision- R1. To mitigate the optimization challenges caused by overthinking after cold start, we propose Progressive Thinking Suppression Training (PTST) strategy and employ Group Relative Policy Optimization (GRPO) with the hard formatting result reward function to gradually refine the model's 's ability to learn correct and complex reasoning processes on the multimodal math dataset. Comprehensive experiments show our model achieves an average improvement of ~-6% across various multimodal math reasoning benchmarks using only 10K multimodal math data during RL training. Vision-R1-7B achieves 73.5% accuracy the widely used Math Vista benchmark, which is only 0.4% lower than the leading reasoning model, OpenAl O1. Scaling up the amount of multimodal math data in the RL training, Vision-R1 R1-32B and Vison-Ro RI-72B achieves 76.4% and 78.2% Math Vista benchmark scores, respectively. The datasets, weight and code will be released in: itttps://githii com /Os 1 / wwviviiin oon-R1
INTRODUCTION
Enhancing the complex reasoning capability of Large Language Models (LLMs) remains one of the most challenging problems in Artificial Intelligence (AI), which is widely regarded as a critical pathway toward Artificial General Intelligence (AGI) (Jacch et al.) 2024 DeepSeek-A]] 2025). Conventional inference paradigms typically rely on a simple "direct prediction" approach to generate concise final answers without explicit, structured intermediate reasoning steps, which often exhibits suboptimal performance complex reasoning tasks (Jaech et al., (024). OpenAl O1 (Jaech et al.) 2024) was the first LLM with strong reasoning ability by using complex Chain-of-Thousee (CoT) for training to achieve significant performance gains over prior LLMs. Meanwhile, various methods (Weil
et al. 2022 Yao et al. 2023
Besta et al., 2024: Lightman et al. 2023 [eeaato et al. 2022 Wang
et al.
2023
Lai et al. 2024 Wan et al. 2024 Trinh et al. 2024 Xin al.) 2024 Muennighofi
et al. 2025
Ye et al. 2025) have been explored to generate high-quality complex CoT reasoning and
further advance the field by optimizing reasoning pathways.

---

Published as a conference paper at ICLR 2026
Length and of Valee-RL-C (NathMMrres)
Benche Asuessy (WerkVost))
"Ahal Masast (30 sumplex MuthWerma)
VQA detsaet
(A)
Frsperiin Aelurey
(9)
(C)
Caaal 5-8
Weeddt Der
1V.45
Vilesii (Din
MLLMI R1
41%
400
7B
788
728
100
Muthioodd
Mede
Average GOT Length Prograssion
Avarage Bandweark Accunacy Progresdion
Reward Prograssion
to new (Dr))
(F)
cal
Facrae or Lagh
Visin
Yrokigg
You s0r1))
10un0
VisianR33
Figure 1: Left panel: Our Vision-RI Pipeline. We first use the existing MLLM and DeepSeck-RI to obtain high-quantity Multimodal CoT dataset, which is used as the cold-start initialization data for the base MIL.M to obtain the post-cold-star Vision-R1-CI and then we perform the RL. training on Vision-RI-CI to obtain the reasoning MLLM, Vision-R1. Right panel: We observe that directly applying RL to MLLMs fails to effectively incentivize strong reasoning capability (see (C) and (D)). Vision-RI-ZA ---Zero, trained via RL without prior initialization, struggles to generalize from limited data (see (E), (F), notably, Vision-R --Zero was applied in format reward function). Vision-R RICI faces the Overthinking Optimization Problem, favoring shorter CoT reasoning, where correct reasoning processes mostly focus on the shorter CoT reasoning sequences (see (A)). During subsequent RL training, we observe a lengthening of reasoning steps but decline in performance (see (D) and (E)), making optimization particularly challenging. For Vision-R1. 1, it initially shortens CoT to refine the right thought process under RL training. PTST enables Vision-RI to progressively acquire a more complex reasoning process (see (C), (D), and (E)) to improve the performance, such that our Vision-RI with 7B parameters achieves comparable performance to the 70B+ strongest MLLMs (see (B)). Note that Vision-R RI used various colored lines indicate the different stages in PTST.
In the field of Multimodal Large Language Models (MLLMs), recent works Yao et al. 2024 Thawakar et al. 2025) have also explored the application of CoT reasoning. These approaches assume that MLLMS lack a structured reasoning process, achieving low performance on the tasks that require logical inference. To improve the reasoning capability of MLLMs, several methods (Xul ct al. 2024 Yao et al. 2024) try to construct the datasets manually containing step-level reasoning processes and apply supervised fine-tuning (SFT) to reformat MLLMs' outputs. However, this manually designed "Formatted Reasoning MLLM" often results in "Pseudo-CoT" reasoning, which lacks essential cognitive processes commonly observed in human thoughts, such as questioning, reflection and inspecting (see Fig. [2, the data examples of "Pseudo-Col" and the complex CoTT) This limitation hinders their application on complex vision reasoning tasks. Thus, it important to generate human-like, high-quality, complex CoT reasoning data for training MLLMs, enabling them to more effectively tackle intricate multimodal reasoning tasks.
Recently, DeepSeek-R1 (DeepSeek-Al, 2025) has successfully applied Reinforcement Learning (RL) to induce the self-emergence of complex cogmitive reasoning ability in LLMs. This begs our rethinking: Can RL be utilized to incentivize the reasoning capability in MLLMs? To answer this question, we first follow the DeepSeek-R1 RZ-eeo paradigm (DeepSeek-Ai 2025). by directly using RL to improve the reasoning capability of MLLMs. Unfortunately, this direct RL. training is challenged, as it struggles to effectively guide MLLMs generating complex CoT reasoning in absence of large-scale, high-quality multimodal data and prolonged training (see Fig. (E) and (F)).
To address the above issue, we propose Vision-RI, a reasoning MLLM that integrates cold-start initialization with RL training. First, we construct high-quality multimodal CoT dataset without requiring manual annotations. Specifically, we leverage an existing MLLM to generate "Pseudo- CoT" reasoning text from multimodal image-text pairs. This "Pseudo-CoT" reasoning explicitly incorporates both vision descriptions and structured step-level reasoning process, exposing more detailed vision information the MLLM to obtain a description including necessary vision information. This process effectively implements "Modality Bridging", converting vision information to language. The resulting textual
textual format. Next, we feed the enriched reasoning text back into
2

---

Published as a conference paper at ICLR 2026
descriptions are then passed to text-only reasoning LLM, DeepSeek-RL. -1 extract high-quality CoT reasoning. Finally, the dataset refined through rule-based data filtering, ultimately obtaining dataset with 200K multimodal human-like complex CoT reasoning samples, which serves as the cold-start initialization dataset for Vision-R1.
Following the DeepSeek-R1 training pipeline, we need apply Group Relative Policy Optimization (GRPO) (Shao et al. 2024, Dee 2025)) on a 10K multimodal math dataset to enhance the model's is reasoning capability. However, as shown in Fig. ] (A) and (D), we observe an overthinking phenomenon in the cold-start initialized MLLM, i.e. the correct reasoning processes tend to be concentrated on shorter CoT reasoning sequences. This issue leads to the optimization problem in subsequent RL training. To address this challenge, we propose Progressive Thinking Suppression Training (PTST) alongside GRPO, which incorporates a formattong result reward function. This approach encourages Vision-R1 to compress CoT reasoning steps early in the RL process, internalizing correct reasoning methods while progressively extending its reasoning duration over time to effectively tackle more complex problems.
Our main contributions can be summarized as follows:
1. We explore how to use R1-like RL for MLLMs and introduce Vision-R1, a reasoning MLLM that leverages cold-start initialization and RI. training to incentivize reasoning capability. is an early exploration that investigates the application of RI-like RL for enhancing reasoning capability in MLLMs and analyzes differences between direct RL training and the combined approach of cold-start initialization and RL training. We believe that our exploration inspire new insights for the community.
2. A high-quality 200K multimodal CoT dataset without human annotations is constructed serve as a cold-start initialization data for MLLMs. We leverage the proposed PTST to GRPO with hard- formatting result reward function, which effectively addresses the overthinking optimization problem in RL training. PTST enables Vision-RI to progressively develop more complex reasoning processes while effectively guiding MLLMs toward enhanced reasoning capability.
3. Notably, despite having only 7B parameters, Vision-RI achieves performance comparable to The-Art
State-of-The-Art (SoTA) MLLMs with over 70B parameters in math reasoning tasks. Furthermore, the Vision-R1-32B and Vision Saiion-R1-200 R1-72B models achieve an average accuracy improvement of around 10% compared the base model on multiple multimodal math benchmarks.
2 RELATED WORK
2.1 LARGE LANGUAGE MODEL REASONING
As researchers have discovered that enabling LLMs to simulate human-like thought processes and perform stepwise reasoning can significantly enhance their performance on reasoning tasks (Jaech) ct al. 2024), extensive work has been dedicated to exploring LLM reasoning methods. These approaches typically rely on human design to format LLM outputs to follow specific steps, such as Chain-of-Thought (CoT) prompting methods (Wei al. (022), plan-based methods like Tree-
of-Thought and Saapp-oof-Tooughhe (Yao et al., 2023 Besta et al. 2024). process-based reward
models (Lightman et al., 2023] [eeaat et | [022 Wang et al. 2023
Lai al. 2024), Monte Carlo 2024; Xin et al. 2024), as well
Tree Search (MCTS) and Beam Search (Wan et al.
2024
Trinh et al.
as constructing SFT datasets (Muennighoff et al.
2025
Ye ct al. 2025).
A recent development, DeepSeek-RI (DeepSeek-Al] 2025). demonstrates that applying large-scale Reinforcement Learning (RL) with formatting and result-only reward functions can guide LLMs toward self-emerging thought processes, producing human-like complex CoT reasoning and achieving significant advantages in complex reasoning tasks. This approach has shown immense potential in the field of Large Language Model Reasoning, however, its application MLLMs remains an open area of inquiry.
2.2 MULTIMODAL LARGE LANGUAGE MODEL REASONING
MLLMs typically map inputs from other modalities to the textual modality, which are then processed by L.MSs. This approach has been proven to exhibit superior performance in range of vision
3

---

Published as a conference paper at ICLR 2026
Modality Bridging
Multimedal
Descrigtion
deepoest +2
Wisen cold
Tringht uoud
key bo
Toln les betwers
Quattor
(ouAcoo commepnndl degeeeddddyy parts mcaet
locade ure congeee
Queetio:
fiurs, poinss A
MILM
Lee proeedie
DeapSeec
breah doen
trem
"gure thon the of 6.15
thoigh Ins, ond
paccepptted db: and
cirosd wettons A8 12
conphent to
dog
Rosoras: succhay let's try
30 and -5 8o
Juten lin wolee 10-33
inage Aeripp but
Leys undl newr choir
of
Beree vm ko
slmei bp
shveld
Figure 2: The overall data generation pipeline incorporating our Modality Bridging method. The multimodal data first sent to MLLMs to obtain "Pseudo-CoT" consisting of a caption and reasoning process, which serves as the input of MLLMs along with the original image-question pair to produce detailed descriptions. Through this modality bridging approach, the textual descriptions provide DeepSeek-ER RI with holistic information that facilitates the generation of high-quality CoT processes, which are post-prees post-processed and integrated with the original data to create the final Vision- R1-cold dataset.
understanding tasks (Liu et al., 2024b Bai et al., 2023: Zhu al., 2023; Huang et al. 2024,
Zhao et al. (024b] a). Inspired by advancements in LLM reasoning. many studies have also sought to enhance the reasoning capability of MILMss For instance, efforts have been made to employ CoT prompting (Zhang al. 2024b Mitra et al., 2024 Luan et al., 2024 and to construct SFT datasets that include step-level reasoning (Yao et al. 22224] Thawakar et al. 2025). However, the CoT generated by these methods often lacks natural human cognitive processes, such as questioning, reflection, and inspecting, which limits their effectiveness in solving complex reasoning tasks. In contrast, Vision-RI distinguishes itself by combining cold-start initialization with RL training to acquire high-quality, complex CoT reasoning capability.
3 METHOD
3.1 CAN ONLY RL INCENTIVIZE REASONING CAPABILITY IN MLLMs?
Inspired by DeepSeek-R -1--eeek-R-Zerre 1-Zero [DeepSeek-Al 2025), we aimed directly use RL to guide models towards self-thought and the emergence of complex reasoning capability. To this end, we collected a dataset of 10K open-source math problems for RL training. Specifically, we followed the DeepSeek- R1-Zero pipeline, training base MLLM using GRPO (Shao et al.) 2024). with output format constraints dictated by the following system prompt:
A conversation between User and Assistant. about the reasoning process
first thinks provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags
For the reward function, we utilized both formatting and result rewards:
Formatting reward function: The model's output must adhere to the "<think> </think>< </answer>" format.
Result reward function: The model's generated final result must align with the ground truth.
We set the formatting and result reward ratio as 1 1.
The model after purely RL training is named as Vision-R1-Zer Unfortunately, as shown in Fig. T (D) and (E), directly applying RL to train MLLMs has proven challenging in stimulating MLLM's reasoning capability and producing lengthy, complex CoT, limiting the model's reasoning ability. Moreover, we observed that training progressed over extended periods, the model gradually learned to use longer reasoning processes to solve hard problems, but this did not show significant performance improvement. Thus, we claim that directly using RL incentivize the reasoning capability of MLLMs for solving complex reasoning problems remains challenging task, especially under constraints of data quality and quantity, as well as computation resources.
4

---

Published as a conference paper at ICLR 2026
Stagel
Stoge?
Shage3
Og
Ref Model
A
Policy
Ox
HERR!
Group Computaation
A2
Model
O
FG
Aa
Progressive Thinking Suppression Training (PTST)
Figure GRPO with our proposed PTST strategy. We progressively loosen the context length restrictions, increasing the length of reasoning process. Specifically, we set the reasoning length to 4K, 8K and 16K tokens for each stage, with corresponding group numbers 16, 8 and respectively, The reward function for GRPO is based on hard formatting result reward function (HFRRF). The dotted line in the "Stage 3" indicates that the final version of Vision-RI -l did not undergo the third stage of training.
3.2 OVERVIEW OF VISION-RI
In above explorations, observed that RL-only approach struggles guide MLLMs in generating human-like complex COT. Consequently, we explored an alternative strategy and introduced the reasoning MLLM, Vision-RI. This method begins with cold-start using multimodal CoT dataset, which initially teaches the base model to reason in "human- like" manner. Subsequently, we apply RL to the cold-start initialized model Vision-R1-CA to guide towards adopting the correct reasoning process, thereby incentivizing the reasoning capability in the final model Vision-R1.
In the following sections, we first describe our approach to create a high-quality, human annotation- free multimodal CoT dataset in Sec. 3.3. well as the Overthinking Optimization Problem faced by post-cold-start MLLMs in Sec. 3.3.2 Then we discuss the RL training method, Progressive Thinking
Suppression Training (PTST) Sec. 3.4 to address the Overthinking Optimization Problem.
3.3
COLD-START INITIALIZATION
3.3.1
MODALITY BRIDGING TO OBTAIN HIGH-QUALITY MULTIMODAL COT DATA
Many existing works (Xu al., 2024 Yao et al., 2024) have attempted to construct multimodal reasoning datasets enhance the reasoning capability of MILMs. Prior efforts typically gathered CoT data which often lack the natural cognitive processes of questioning, and self-reflection. These datasets typically constructed in step-by-step form based human heuristics. However, goal is to collect a multimodal CoT dataset that encompasses complex cognitive processes teach Vision-R1 reason in human-like, natural manner. Furthermore, DeepSeek-R1 has demonstrated the ability to generate CoT with natural cognitive processes and has proven to have strong reasoning capability. By using the high-quality CoT data it generates, which incorporates human-like cognitive self-reflection processes, we can train MILMs to enhance their reasoning capability. However, being "text-only" LLM, DeepSeek-RI struggles to effectively process multimodal inputs produce high-quality COT
To overcome these limitations, we utilize existing MLLMs alongside DeepSeck-RR and propose method named Modality Bridging to indirectly convert multimodal information into textual infor- mation, thereby capturing the complex cognitive processes of DeepSeek-Ri
the data generation
pipeline that shown in Fig. firstly, we follow prior works (Xu et al.) 2024] Yao et al. 2024) by inputting an image-question-ansee -anserr pair and a prompt into MLLM to generate a "Pseudo-CoT" featuring both image description and reasoning processes. Subsequently, we concatenate the image- question pair with the "Pseudo-CoT" and prompt, and then feed them into a MLLM to obtain a detailed image description. Below is the prompt template:
Given a image, a question: ( and a thinking process: (thinking process), provide detailed description containing all the necessary details of the image to answer the question correctly...
This process of generating "Pseudo-CoT" explicitly exposes more of the necessary vision details for reasoning in textual form, compared to pure image descriptions. This assists the MILLM producing
5

---

Published as a conference paper at ICLR 2026
Table 1: Comprehensive comparison with SoTA MLLMs (closed-source, open-source gen- cral/maath/reasoning MLLMs) across diverse multimodal math benchmarks." "Avg." denotes the average performance over all benchmarks. For Math Vista benchmark, have specifically compared all models on three sub-tasks that are highly related to mathematical reasoning: geometry reasoning (GEO), algebraic reasoning (ALG), geometry problem solving (GPS) and math word problems "ALL"
(MWP). "ALL"" denotes the average score on Math Vista benchmark. The best results are bolded.
Model
Params.
MatiVista
Matheese
DynaMath MM-Marh
Avg.
GEO
ALG
GPS
MWP
ALL
Closed Source MLLMs
OpenAI
Jaech el
73.91
(Rest
63.8
37.6
64.9
GPT 1-4
Opest AVS
49.9
39.8
23.1
37.5
Chande-3. Sentier Aumin VIM
2024
67.7
26.5
62.5
Open Source General MLLMs
Qwen2.5 Qwen2.5-V1-70 5-V1-
Rii 58 7625
7B
66.9
68.7
66.8
76.9
68.1
46.7
34.1
50.7
49.9
Owen2.5 5 -LVVLL332 320
320
72.8
13.7
75.0
72.6
72.9
52.3
34,9
55.5
53.9
Quen2. V-V--73
74.7
73.5
61.2
$7.9
LaeraVL25-788
788
76.3
77.9
75.8
71.9
25.4
41.4
39.1
Open Source Math MLLMy
Loab-LLaVA--1 13B cf al 2011
13B
56.5
40.2
57.7
56.5
16.5
22.9
Math PUMA-Qweni 78 7rmmm
0024
7B
47.3
48.1
47.9
33.6
Multimath 7B3 (Penk cra K020
78
66.8
61.8
50,0
26.9
URSA-SB SB
Loe
8B
79.3
75.3
59.8
45.7
Source Reazoning MLLMS
LLAAA-CoT-I Xa 2004
11B
54.8
34.6
31.6
Mullben--78 Moraar
63.1
23.7
Model
Vision RI (Ours)
78
80.3
79.0
33.2
80.6
13.5
52.4
40.2
56.3
35.6
A (vs Qwen2.5 VL-7B)
+13.4
+10.3
+16.4
+3.7
+3.4
+3.7
+6.1
+5.6
+3.7
Vision: RI 32B (Dus)
32B
85.8
82.6
$8.0
78.5
76.4
62.1
55.3
65.6
64.9
(vs Qwen2 VL-BB2
+13.0
+8.9
+13.0
+5.9
+1.7
+9.8
+20.1
+10.1
+11.0
Visice-RI R1-728 (Oan)
89.4
79.6
78.2
66.4
66.8
V-V22B2)
+1.9
+..7
+5.2
The resull collected from the official MathVista lesdetbourd (sttps:/ httppe://ncthviii www.au..tttt.......... www.hhbb./////e losoooooooo
Ti uses odditionnl data during RL miningg
more detailed descriptions, thereby minimizing information loss during the conversion of original
multimodal information to textual information (see Fig. 5
At this stage, we cleverly bridge image information to textual information and feed it into DeepSeek- R1 obtain high-quality CoT processes. We then retain reasoning processes from the generated multimodal CoT data that the final answer is aligned with ground truth and apply rule-based data filtering to remove logical inconsistent samples and replace some words for semantic coherence.
Finally, we pair the pure text CoT data generated by DeepSeek-RI from the above process with the corresponding images, integrating into multimodal CoT data, named Vision-RI RI-cold dataset. This dataset is used for the cold-start initialization of Vision-RI R. By acquiring CoT data in this manner, which closely mimics human cognitive behavior, the reasoning processes exhibit natural thinking.
3.3.2 OVERTHINKING OPTIMIZATION PROBLEM
After obtaining a multimodal CoT dataset, we conducted SFT on a pretrained MLLM (such as Qwen2.5-VL (Bai et al. 2025)) as the base MLLM for cold-start initialization. The MLLM after cold start initialization is named as Vision-RI-CI At this stage, the base MLLM had learned the complex reasoning mode from DeepSeek-Re R1, however, this led the Overthinking Optimization Problem, i.e,, Vision-R1-CI would engage in prolonged thought processes on certain problems, whereas the correct reasoning processes were typically concentrated in shorter cognitive chains.
As shown in Fig. [ (D) and (E), this propensity for excessive incorrect reasoning significantly complicates the optimization of subsequent RL training. For instance, when the allowed thought length during RL training for Vision-RI-CC was directly extended to 16K, the model tended to generate longer answers to fulfill the demands of complex reasoning. However, this incorrect reasoning did not lead to performance improvements, thus presenting challenges in incentivizing reasoning capability in MLLMs. So, is crucial to guide the model to learn correct thinking in the early stages for the reasoning performance of MIL.Ms.

---

Published as a conference paper at ICLR 2026
Table 2: Comparison with SoTA MLLMs across diverse comprehensive multimodal benchmarks. The base MLLM is Llama-3.2-1 I- (Dubey et al. 2024). The results indicate that our data significantly enhances the generalization capabilities of our model, leading to superior performance across all benchmarks.
Method
General Benchmark
Math Benchmark
MMStar
CharQQ
MME.sm
HallBench
MathVista
MathiVere
MM-Maih
LAuma-3 2-1
Dubyy et all 2024
49.8
83.4
1787
40.3
48.6
8.4
4.1
Mulberry-LLnn I Liama- 1BB Yon 2024
58.5
83.5
2005
48.9
61.1
18.2
LLaVA.Cot: 1B (X
at 2024
57.6
81.9
2137
47.8
54.8
20.3
Vision Cl
3.4 PROGRESSIVE THINKING SUPPRESSION TRAINING
Inspired by the above phenomenon, we propose the Progressive Thinking Suppression Training (PTST) algorithm to initially suppress the length of reasoning during the early stages of RI. training for Vis while guiding towards the correct reasoning processes. As training progresses, gradually relax these constraints, allowing Vision-RI to autonomously learn to utilize longer CoT to address increasingly complex problems, thereby enhancing its reasoning capability. Specifically, we implement Group Relative Policy Optimization (GRPO) with hard formatting result rewards for the model's self-learning. Consider the standard GRPO approach, it samples a group of generated output set {0r, 02, ... .GG} for each question from policy model To. Then GRPO maximizes the following objective and optimizes the model Te.
(0) = Bg)r()), (m (O1g)
min To(o: [g) KRaia (o: q) Teala (o, 19)11 -,,, 2.1+te A.) - DKu,(TO Tiaett
A,, clip To g)
(1)
where and are the PPO clipping taper-parammetr and the coefficient controlling the Kull- back-Leibler (KL) penalty (Shao et al. 20224, Schulman et al. 2017). respectively. We set ==0.2 and ff=le22 during training. A, = Ti- m--an (Ti (um.rol is the computed advantage using the group
std std( (2..a...
rewards (ri,72: T2, ... ,rc) and Dr ll (e)))) Woto;lg)
Tar(o, lg) log
[ar[or]g]
1 is the KL divergence. Mo(0.14)
As shown in Fig. 3 in our proposed PTST, we denote the total number of training stages by S. with each stage having its own sampling count G, and sequence length limit L,. The output space for
stage {1. 2. 2.... $} O(*) =f No: |o| S }.}. The training objective for the s-th stage can be further reformulated based on Eq. as:
WPIST (0) quP(R) 1o (0(")) Te (o(-)1))
GRPO
(0 (*) lg) 1 chip Te(o!") q) 1- 1+e) DRI. DKI((TO mar)
(2)
G. min
(o
To 12)
where (*) denotes the advantage estimate for the i-th sample in the training stage 8, and Tea denotes the policy model with an output length constraint of O(s). We employ the hard formatting result reward function as the reward mechanism for GRPO, i.e., the model receives reward score of r, = 1 only when both the formatting requirements and the correctness of the final answer are simultaneously satisfied; otherwise, it receives score of r; = 0. Moreover, we do not impose constraints using system prompt, as Vision-RI-CI has already acquired robust formatting capability during the cold-start initialization.
By applying PTST, we compress the model's thought length in the early training stages to guide correct reasoning and gradually relax these constraints in later stages. As illustrated in Fig. [ this progressive strategy enables Vision-RI to generate more complex CoT and significantly enhances its reasoning capability. Notably, in practice, performance by the end of the second training stage, leading us to select as the final stage, i.e., we set the parameters S 2, L., E (4K, (4K,8K, 16K), and G, E (16, 8, 4), respectively.
observe that Vision-RI achieves competitive
7

---

Published as a conference paper at ICLR 2026
deatom "gure parcle
dowe foor, AB tegest bn
Atatom Ab undiA
Chocen 856 DEY
AC atb This the duy is of coch EoC
mapi thay prblen chpleys
Ohay preieon
rass la lad
lom foot
porpeciode the rod pait
from Wan the pe ward our
pust Wan
4KC onde D8A port fosed
cajs EET calin and alll soed lesntt Eo
Let's 1DC and not right
capa optind Con bo
con. Allemitiely
theck
-omeaaaaannn Fre Anawer varrean
wraaooaaeee- Fnn Abemend vieever
Figure 4: The output examples of Vision-R1-7B "human-like" questioning and self-reflective thought process when solving math reasoning problems, which is also called "Aha moment" in DeepSeek-R1 I's paper (DeepSeek-A] 2025). More examples are provided in suppiementary materials.
Math Verse benchmark. Vision-R1-22 shows
4 EXPERIMENTS
Please refer to Appendix A to obtain the detailed data and training settings. We provide the main results of Vision-R1 in Sec 4.1 and then introduce the detailed ablation study in See 4.2
4.1 MAIN RESULTS
As shown in Tab. 1 our proposed Vision-R1-7B achieves competitive results across multiple math reasoning benchmarks, even when compared to SoTA models with over 10 times the pa- rameters of Vision-RI 11-7B. For instance, on the MathVista benchmark, Vision-R1-7B achieves a score of 73.5%, only 0.4% lower than OpenAI 01, the most widely recognized reasoning model. Morcover, on the complex math reason- ing sub-tasks of Math Vista, i.e., GEO, ALG, and GPS, Vision-R1-77 achieves scores of 80.3%, 79.0%, and 83.2%, respec- tively, exceeding the base model Qwen-2.5- VL-7B by an average accuracy improve- ment of over 10%. These results high- light the strong reasoning capability Vision- R1-7B gains through "human-like" com- plex thinking processes. Furthermore, on the more challenging Math Verse and MM- Math benchmarks, Vision-R1-7B ranks Top-1 and Top-2, respectively, with the lat- ter being second only to 1wwn-22.5VL--11 This demonstrates Vision R 1-7B's 's effectiveness in solving complex math problems.
Table 3: Evaluating the impact of Cold Start Initializa- tion, GRPO, Progressive Thinking Suppression Train- ing (PTST). "Avg. Len." denotes the average output token length on the 10K multimodal math dataset for RL training. "Avg. Acc." denotes the average perfor- mance (Math Vista, Math Verse, MM-Math).
Method
Cold Start GRPO PIST Avg. Len.
Avg. Acc.
Vision-RI Ri-roo
1285
50.7
Visien RI-CI
3566
44.5
Vision-RI Fi-oonRR-Loon
3107
47.7
Vision RI (Ours)
2057
55.4
4.2 ABLATION STUDY
Cold-start Dataset Quality. We conduct a quality analysis of our proposed Vision-RI R1-cold dataset. The primary objective of constructing datasets, which lack complex cognitive processes, and to leverage DeepSeek-R1's high-quality CoT process as cold-start data. To evaluate this, we present a comparative analysis in Tab. [ where we statistically examine the presence of questioning, reflection, and inspection within the CoT processes of Mulberry, LLaVA-CoTE and Vision -colld datasets. The results indicate that Vision-R -I-oo- contains significantly higher proportion of human-like cognitive processes compared to previous multimodal CoT datasets. This complex CoT structure facilitates the base MLLM in learning reasoning mechanisms, providing high-quality cold-start initialization for RL training.
is to supplement the existing multimodal CoT
Additionally, we compare Vision-RI Rl-ll with previous multimodal CoT datasets by training on Llama-3.2-4 1-B--BB-V in Tab. After SFT, Vision-RI-LLamad V-CI-1 1B model achieves SoTA
8

---

Published as a conference paper at ICLR 2026
Table 4: Comparison of the occurrence frequency of self-reflective indicators between llava-cot, mulberry and our Aiion-RI-colle dataset. The higher frequency of these reflective markers in our dataset demonstrates its distinctive self-reflection and self-correttion characteristic.
llava-cot (100K)
Mulberry
Vision-RI-cold
Word
(260K)
(200K)
"Wait"
2,300
1,122
585,719
"Hmm"
0
75,853
"Mistake"
183
8,784
26,697
"Aternatively"
251
68
188,187
"Check"
8,332
26,421
100,148
Table 5: Effect of PTST. "4Kx16" denotes a PTST setup where the Stage limits the response length 4K with 16 samples. "Avg." denotes the average performance (Math Vista, Versee MM-Math).
Method
Stage
Stage 2
Stage
MathVisita
MathVerse
MM-Math
Avg.
Baseline
68.1
46.7
34.1
49.6
4Kx
4Kx16
72.6
51.4
39.0
54.3
8Kx16
55.3
46
6Kx12
8Kx8
73.0
52.6
39.8
55.1
Vision R1-L.ong
16K
16Kx4
70.3
36.8
36.1
47.7
16
16Kx16
36.5
47.9
Vision RI (Ours)
16
8Kx8
73.5
52.4
40.2
55.4
performance across all general and math reasoning benchmarks, outperforming both LLaVA-CoT- 11B and nuulbrrryLLlama- IB, directly confirming the superior quality of Vision-RI-cole dataset.
Effect of Main Strategy. As shown in Tab. 3 we compare the performance of various RL training strategies. The results indicate that Vision-R1-Zen
which applies RI. training directly without cold-start initialization, struggles to generate sufficiently long and complex CoT reasoning, thereby limiting its ability to handle intricate reasoning tasks. contrast, Vision-RI ICI, after cold-start initialization, tends to generate excessively long CoTs. However, the presence of numerous incorrect reasoning steps leads to lower overall performance. Furthermore, Vision-RI Dision-RI-Long via applying RL training directly to Vision-R1-CA results in optimization difficulty, making it hard to achieve significant performance improvements. In comparison, our proposed Vision-RI demonstrates a substantial advantage in reasoning performance, effectively balancing CoT complexity and accuracy.
Effect of PTST. Tab. our two-stage setup (4K 16-8K x) achieves the best average (55.4%) x 14Kx16->44 16) by +1.1 Avg. (54.3->55.
outperforming training with fixed short length
indicating that progressively relaxing the length constraint in Stage 2 yields consistent gains once correct thinking is established in Stage In contrast, training with fixed 16K (16Kx4 or 16K x 16) severely underperforms overthinking. Further increasing Stage 2 sampling to 16 (4Kx16-883 16, 55.3%) or inserting an extra stage (4Kx 16-+6K 12-8Kx88, 55.1%) brings no meaningful improvement. suggesting PTST robust and a simple two-stage schedule suffices under matched training time (sampling xlength kept constant per stage).
7%%44.9%) .9%), showing that early length constraints effectively mitigate
Effect of Cold Start. Tab. @emonsttrates that PTST alone offers limited benefit without cold start (Zero+PTST: 31.8% vs Vision-RI R--eer: 50.7%), and SFT without CoT before RL is detrimental STTT 39.8%). In contrast, cold-starting on Vision-RI R--ll followed by PTST yields the best average (55.4%), with substantial gain MM-Math (40.2% vs 33. 13.1%/22..89 8%). These results support that complex CoT priors acquired in cold start are essential for RL to learn correct reasoning patterns. Furthermore, PTST then suppresses early overthinking (short 4K) and safely extends reasoning length (8K), leading to consistent improvements.
Visualization. As shown in Fig. 4 proposed Vision-R1 -1-BB is capable of generating complex reasoning processes and exhibits the emergence of the so-called "Aha moment" (DeepSeek-AT) (2025) i..., a phenomenon analogous to human cognitive processes involving questioning and reflection. This sophisticated reasoning capability significantly enhances the model's inference performance, leading to substantial improvements in solving complex reasoning tasks.

---

Published as a conference paper at ICLR 2026
Table 6: Effect of Cold Start. "Zero+PTSTT denotes we keep the Vision-RI-Zero settings while adopting PTST strategy. "ZET involves training the base model in the Vision-RI- cold dataset without CoT annotations before RL training, followed by RL training with the same "Zero+PTST" setup. "Avg." denotes the average performance (Math Vista, Math Verse, MM-Math).
Method
MathVerse Visiaa
MM-Math
Avg.
Vision-Ri RI RI-Zero
70.6
52.6
28.8
S0.7
Zero+PTST
71.3
50.9
33.1
51.8
SFT.SST
68.7
32.0
18.9
39.8
Vision-RI (Ours)
73.5
52.4
40.2
55.4
CONCLUSION
We explore how to use RL training to incentivize reasoning capability in MLLMs. Moreover, we proposed Vision-RI and achieved the strong math reasoning ability, achieving comparable performance to SoTA MILMS.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Science Foundation of China (NO. 62572193), the Open Research Fund of the Key Laboratory of Advanced Theory and Application Statistics and Data Science, Ministry of Education, and the Fundamental Research Funds for the Central Universities.
REFERENCES
Anthropic.
Claude 3.5 sonnet.
http:://w..attboogcc.coo./
claude - 3- 5- sonnet 2024.
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pp. 2425-2433, 2015.
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308. 012.308..2299 /2966, 2023.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang. Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang. Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang. Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint 2502 13923, 2025.
Macicj Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi. Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 17682-17690, 2024.
Huanqia Cai, Yijun Yang, and Winston Hu. Mm-iq: Benchmarking human-like abstraction and reasoning in multimodal models. arXiv preprint arXiv:2502. 00698, 2025.
Jie Cao and Jing Xiao. augmented benchmark dataset for geometric question answering through dual parallel text encoding. In Proceedings of the 29th international conference on computational linguistics, pp. 1511-, 2022.
Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P Xing, and Liang Lin. Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning. arXiv preprint arXiv:2105.1451 14517. 2021.
Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen, and Xiaodan Liang. Unigeo: Unifying geometry logical reasoning via reformulating mathematical expression. arXiv preprint 02.22722 2022.
10

---

Published as a conference paper at ICLR 2026
Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modaa models with better captions. In European Conference on Computer Vision, pp. 370-387. Springer, 2024a.
Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision- -sionnlanguage models? arXiv preprint arXiv:2403.20330 2024b.
Zhe Chen, Weiyun Wang. Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271, 2024c.
DeepSeek-AL. Deepseek-r1: Incentivizing reasoning capability in Ilms via reinforcement learning, 2025.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad AI-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint urXiv:2407.21783 2024.
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, and Rongrong Ji. Mme: A comprehensive evaluation benchmark for multimodal large language models. ArXiv, abs/2306. abs/2306.11339,, 2023. URL https://app semant semanticscho aarooaar-a www.laarrrrroopoereeeeee Corpus 259243928
Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye. Wanjun Zhong. Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, et al. G-llava: Solving geometric problem with multi-modal large language model. arXiv preprint arXiv:23 1370, 2023.
Yash Goyal, Tejas Khot, Douglas Summers-Stee Stay, Dhruv Batra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 6904 01144,9933222 2017.
Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, et al. Hallusionbench: an advanced diagnostic suite for entan- gled language hallucination and visual illusion in large vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14375-14385, 2024.
Jarvis Guo, Tuney Zheng, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Yizhi Li, Graham Neubig, Wenhu Chen, and Xiang Yue. Mammoth-vit Eliciting multimodal reasoning with instruction tuning at scale. arXiv preprint arXiv:2412.05237. 2024.
Himanshu Gupta, Shreyas Verma, Uijwala Anantheswaran, Kevin Scaria, Mihir Parmar, Swaroop Mishra, and Chitta Baral. Polymath: A challenging multi-modal mathematical reasoning bench- mark. arXiv preprint arXiv:2410. 14702, 2024.
Danna Gurari, Qing Li, Abigale Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham. Vizwiz grand challenge: Answering visual questions from blind people. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3608-3617, 2018.
Wenxuan Huang, Zijie Zhai, Yunhang Shen, Shaoshen Cao, Fei Zhao, Xiangfeng Xu, Zheyu Ye, and Shaohui Lin. Dynamic-llava: Efficient multimodal large language models via dynamic vision-language context sparsification. arXiv preprint arXiv:2412.00877 2024.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024.
Aaron Jacch, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El- Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai ol system card. arXiv preprint arXiv:2412. 16720, 2024.
11

---

Published as a conference paper at ICLR 2026
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 2901-2910, 2017.
Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. Dvqa: Understanding data visual- izations via question answering. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 5648-5656, 2018.
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, and Yoshua Bengio. Figureqa: An annotated figure dataset for visual reasoning. arXiv preprint arXiv:1710.07300. 2017.
Mehran Kazemi, Hamidreza Alvari, Ankit Anand, Jialin Wu, Xi Chen, and Radu Soricut. Geomverse: A systematic evaluation of large models for geometric reasoning. arXiv preprint 1rX1::2312.12241, 2023.
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Sco, Hannanch Hajishirzi, and Ali Farhadi. A diagram is worth dozen images. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV 14, pp- 235-251. Springer, 2016.
Aniruddha Kembhavi, Minjoon Sco, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi. Are you smarter than sixth grader? textbook question answering for multimodal machine comprehension. In Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pp. 4999-5007, 2017.
Xin Lai, Zhuotao Tian, Yukang Chen, Senqiao Yang, Xiangru Peng, and Jiaya Jia. Step-dpo: Step- wise preference optimization for long-chain reasoning of llms. arXiv preprint arXiv:2406. 18629, 2024.
Jason J Lau, Soumya Gayen, Asma Ben Abacha, and Dina Femner-Fushman. A dataset of clinically generated visual questions and answers about radiology images. Scientific data, 5(1):1-10, 2018.
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli. Dual-glance model for deciphering social relationships. In Proceedings of the IEEE international conference computer vision, pp.
2650-2659, 2017.
Zhuowan Li. Xingrui Wang. Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, and Alan L. Yuille. Super-clevr: A virtual benchmark to diagnose domain robustness in visual reasoning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14963-14973, 2023.
Zhenwen Liang. Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, and Xiangliang Zhang. Scemqa: A scientific college entrance level multimodal question answering benchmark. arXiv preprint arXiv:2402.05138, 2024.
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards. Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let's verify step by step. In The Twelfth International Conference Learning Representations, 2023.
Adam Dahlgren Lindström and Savitha Sam Abraham. Clevr-math: A dataset for compositional language, visual and mathematical reasoning. arXiv preprint arXiv:2208.05:: 2022.
Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. Mitigating halluci- nation in large multi-modal models via robust instruction tuning. arXiv preprint arXiv:2306. 14565, 2023.
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
2024a.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024b.
12

---

Published as a conference paper at ICLR 2026
Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, and Song- Inter-gps: Interpretable geometry problem solving with formal language and symbolic reasoning. arXiv preprint arXiv:2105.04165, 2021a.
Long-Chun Zhu.
Pan Lu, Liang Qiu, Jiaqi Chen. Tony Xia, Yizhou Zhao, Wei Zhang. Zhou Yu, Xiaodan Liang, and Song- Long-Chun Zhu. Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning. arXiv preprint arXiv:2/ 132144, 2021b.
Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai- wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507-2521, 2022.
Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng. Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv prepriat arXiv:2310.02255, 2023a.
Pan Lu, Liang Qiu, Wei Chang, Ying Nian Wu, Song-Chin Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. In International Conference on Learning Representations (ICLR), 2023b.
Bozhi Luan, Hao Feng, Hong Chen, Yonghui Wang, Wengang Zhou, and Houqiang Li. Textcot: Zoom in for enhanced multimodal text-rich image understanding. arXiv preprint arXiv:2404.09797, 2024.
Ruilin Luo, Zhuofan Zheng, Yifan Wang, Yiyao Yu, Xinzhe Ni, Zicheng Lin, Jin Zeng, and Yujiu Yang. Ursa: Understanding and verifying chain-of-thought reasoning in multimodal mathematics. arXiv preprini 1XXi::5001.06666 2025.
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A bench- mark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203. 2022.
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 2200-22002222 2200,220,,2220 2021.
Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawahar. Infographicvqa. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1697-1706, 2022.
Nitesh Methani, Pritha Ganguly, Mitesh M Khapra, and Pratyush Kumar. Plotqa: scientific plots. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1527-1536 2020.
Reasoning over
Chancharik Mitra, Brandon Huang, Trevor Darrell, and Roci Herzig. Compositional chain-of-thoughl prompting for large multimodal models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 14420-14431, 2024.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannanch Hajishirzi. Luke Zettlemoyer, Percy Liang. Emmanuel Candès, and Tatsunori Hashimoto. sl: Simple test-time scaling. arXiv preprint arXiv:2501.19392 2025.
OpenAl.
Gpt-ov(ision) system card.
https://openai.com /ecio///inn//////
gpt 4v- wwwtsmssttmmmmm card 2023.
Shuai Peng, Di Fu, Liangeai Gao, Xiuqin Zhong, Hongguang Fu, and Zhi Tang. Multimath: Bridging visual and mathematical reasoning for large language models. arXiv preprint 47 2024.
Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Zhuoma GongQue, Shanglin Lei, Zhe Wei, Miaoxuan Zhang, et al. We-math: Does your large multimodal model achieve human-like mathematical reasoning? arXiv preprint arXiv:2407.00 200222077.222. 0/844, 2024.
13

---

Published as a conference paper at ICLR 2026
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
Dustin Schwenk, Apoory Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. A-okvqa: A benchmark for visual question answering using world knowledge. In European conference on computer vision, pp. 146-162. Springer, 2022.
Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni, and Clint Malcolm. Solving geometry problems: Combining text and diagram interpretation. In Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 1466-1476, 2015.
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi. Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 2024.
Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibín Lin, and Chuan Wu. Hybridflow: flexible and efficient rlhf framework. arXiv preprint arXiv: 2409.19256, 2024.
Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang. See-Kiong Ng. Lidong Bing, and Roy Ka-Wei Lee. Math-LLaVA: Bootstrapping mathematical reasoning for multimodal large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 4663-4680, November 2024.
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8317-8326, 2019.
Kai Sun, Yushi Bai, Ji Qi, Lei Hou, and Juanzi Li. Mm-math: Advancing multimodal math evaluation with process evaluation and tme-grained classification. arXiv preprint arXiv:2404.0509) 2024.
Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, et al. Llamav-ol: Rethinking step-by-step visual reasoning in llms. arXiv preprint (xXx:25001006886 2025.
Trieu Trinh, Yuhuai Wu, Quoe V Le, He He, and Thang Luong. Solving olympiad geometry without human demonstrations. Nature, 625(7995):476-455 2024.
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process-and outcome-based feedback. arXiv preprint arXiv:2211.14275. 2022.
Ziyu Wan, Xidong Feng, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, and Jun Wang. Alphazero-like tree-search can guide large language model decoding and training. In Forty-first International Conference on Machine Learning, 2024.
Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. Measuring multimodal mathematical reasoning with math-vision dataset. Advances in Neural Information Processing Systems, 37:95095-955 951699, 2025.
Peiyi Wang, Lei Li, Zhihong Shao, RX Xu, Damai Dai, Yifei Li, Deli Chen, Y Wu, and Zhifang Sui. Math-shepherd: A label-free step-by-ster verifier for Iims in mathematical reasoning. arXiv 2023.
preprint arXiv:2312.088
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022.
Huajian Xin, ZZ Ren, Junxiao Song. Zhihong Shao, Wanjia Zhao, Haocheng Wang, Bo Liu, Liyue Zhang, Xuan Lu, Qiushi Du, et al. beepseck-prover-vl. 5: Harnessing proof assistant feedback for reinforcement learning and monte-carlo tree search. arXiv preprint 2rXiv:2408.08/55. 2024.
Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. Llava-ol: Let vision language models reason step-by-step. arXiv preprint arXiv:241 104400, 2024.
14

---

Published as a conference paper at ICLR 2026
Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song. Haocheng Feng. Li Shen, et al. Mulbenry: Empowering milm with ol-like reasoning and reflection via collective monte carlo tree search. arXiv preprint arXiv:2412.183199 2024.
Shunyu Yao, Dian Yu, Jeffreyy Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, Advances in neural information processing systems, 1809-18811122, 2023.
Yixin Ye, Zhen Huang. Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. Limo: Less more for reasoning. arXiv preprint arXiv:2502.03387, 2025.
Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal Ilm truly see the diagrams in visual math problems? In European Conference Computer Vision, pp. 169-186. Springer, 2024a.
Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang. and Yiming Yang. Improve vision language model chain-of-thie of-thoughttttttt reasoning. arXiv preprint arXiv:2410. 16198, 2024b.
Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Weixiong Lin, Ya Zhang, Yanfeng Wang, and Weidi Xie. Pme-vqa: Visual instruction tuning for medical visual question answering. arXiv preprint 23di::2305.001155 2023.
Cairong Zhao, Yubin Wang, Xinyang Jiang, Yifei Shen, Kaitao Song, Dongsheng Li, and Duoqian Miao. Learning domain invariant prompt for vision-language models. IEEE Transactions on Image Processing, 33:1348-1366 2024a.
Fei Zhao, Taotian Pang, Chunhui Li, Zhen Wu, Junjie Guo, Shangyu Xing, and Xinyu Dai Aligngpt: Multi-modal large language models with adaptive alignment capabilityy arXiv preprint arXiv:240S.14129, 2024b.
Yilun Zhao, Yunxiang Li, Chenying Li, and Rui Zhang. Multihiertt: Numerical reasoning over multi hierarchical tabular and textual data. arXiv preprint arXiv:2206.01344 2022.
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Pro- ceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume System Demonstrations), Bangkok, Thailand, 2024. Association for Computational Linguistics. abs/ 13372
URL httptttpp//axxxii.ororrtt
Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangehi Feng, Dongdong Kuang, and Yuwen Xiong. Easyrl: An efficient, scalable, multi-modalle rl training framework. httts::/githutt com/
hi hiyouga/Ease (aanga/EEasR11 2025.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: En- hancing lasion-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023.
Wenwen Zhuang, Xin Huang, Xiantao Zhang, and Jin Zeng. Math-puma: Progressive upward multimodal alignment to enhance mathematical reasoning. arXiv preprint 2024.
15

---

Published as a conference paper at ICLR 2026
A EXPERIMENT SETTINGS
Dataset and Benchmarks. To obtain the cold-start dataset, we use the multimodal visual question answering (VQA) datasets, LLaVA-CoT dataset (100K) (Xu et 2024) and Mulberry dataset
(260K) Yao et al. 2024), to conduct Vision-RI Miion-RI-cold (2 During GRPO process, we mix the
2025). Polymath (Gupta et 2024). SceMQA (Liang et | 2004), Geometry3K (Lu et al. [02Iaa as our RL training data. Total
math datasets of We-Math (Qiao et al. 2024). Math MathVision (Wang
amount of data around 10K. For scaling up the RI. dataset for Vision-R1-32 R1-32B and Vision-R1-72B,
we render text-based AIME data before 2024 as images, extract subset from MAmmoTT-V (Guo
ct al.
2024) and MMIQ
(Cai et al.
2025). obtaining a total of ~20K additional data.
For evaluating the reasoning capability of our Vision-RI, we choose three widely used multimodal math benchmarks: MM-Math (Sun et al., 2024), Math (Lu et al. 2023a), Math Verse ZZhng
et al. 2024a). These benchmarks covers various mathematical fields which can provide a thorough evaluation of MLLMs' math reasoning ability. Besides, we select four general multimodal bench- marks to demenstrate the general abilitt of our model: MMStar (Chen et al. 2024b), ChartQA (Masry) it al. 2022), MME (Fu et al.) 2023) and HallBench (Guan et al. 2024). Those general benchmarks
used to evaluate the data quality of our proposed Vision- 1-cold dataset.
Implementation Details, For the Mision-R-colle dataset preparation, we deploy the open-source MLLM Qwen2.5-VL-72B (Bai al. 2025) and the reasoning LLM DeepSeek-RI (DeepSeck-AI We then process the VQA datasets using Qwen-2.5-VL-72BB and DeepSeek-RI.
2025).
For the cold-start initialization of Vision-R1, we adopt Qwen2.5-VL. (Bai et al. 2025) the base model and train it via supervised fine-tuning (SFT) for 2 epochs using Llama-Factor frame- work (Zheng et al.] 2024). After cold-start initialization, we obtain the post-cold-start model, Vision-RI-CI, which is subsequently trained on the collected math dataset using GRPO in Verl training framework (Sheng et al. 2024, ZZenng et al. 2025). while following the two-stage PTST approach (note that we do not use the third stage checkpoints), i.e., S was set to 2. The all models in the paper are summarised as follows:
Vision-Rl-Zers
This represents the baseline approach where reinforcement learning (RL) is applied directly to the base MLLM without cold-start initialization. We adopt the limit of 4K generation token length with 16 samples to train the base model via RL training. The system prompt and the training method are described in Sec. 3.1 The full training step is set to 300.
Vision-R1-CI: The base MLLM is cold-start initialized using the Vision-RI-con dataset, resulting in this model.
Vision-RI Fision-Rl-Loog RoLoo This variant is trained with a maximum generation length of 16K tokens, where four samples per input generated from the cold-started Vision-R1-CI, followed by 300 training steps.
Vision-R1: This model follows the Progressive Thinking Suppression Training (PTST) strategy, where a two-stage RL training process is applied:
Stage 1:
The model is trained from Vision-RI-CI for 100 steps with 4K token generation limit, sampling 16 samples per input.
-Stage 2: Training continues for another 100 steps with maximum generation length of 8K tokens, sampling 8 samples per input.
The final model checkpoint at the end of Stage is the final Vision-RI model, as this stage achieves an optimal balance between reasoning length and overall performance. Note that for Vision-R1-33 and Vision-R1-P5 R1-72B, we use additional data continue training them in the RL stage under the same PTST Stage 2 settings.
Additionally, Vision-RL R1 can be further extended to a third training stage, following the same parameter settings Vision-RR-Loon and continuing for additional 100 training steps. However, as shown in Fig. [ further training does not yield significant performance improvements but does generate more complex reasoning processes.
For the ablation study experiments in Sec 4.2 we choose Vision-R1-78 as the default to evaluate. In Tab. and Tab. 6 we chose the optimal checkpoint for reporting performance by evaluating every
16

---

Published as a conference paper at ICLR 2026
Simple Description
The image is bar chart displaying the infont mortality in Kiribati from 2009 2019. The vertical axis represents the deaths per 1,000 births, while horizontal axis lists the years. Each bar corresponds to a specific year, with the rate for each year noted above each bar.
need to check..... Wait, but original data isn't here.....War.. maybe there's s mistake....Hmm, this is wroblem...AAlerrattive. maybe the answer "34,2" per real data, but can't confirn...Howeverr should that this is assumption since the image data isn't provided.
DeepSeek -R1
Detailed Description
The image is a bar chart displaying infant montality rate in Kiribati from 2009 to 2019 The rates for each
Okay, let's see. The question.....irstt I'll recall the data points given..... need to confirm.....et me check again.....So, answer should be stroightforward....iii clearly stated as 40. should make sure..... Therefore, answer
year are as follows: 49.9 2009 ..099.....4...... 40.1 in 2019. The bar for 2019 clearly indicates the infant mortality rate as deaths per 1,000 live
DeepSeek -R1
births.
Figure 5: Comparison between the CoT processes generated by descriptions with and without "Pseudo-CoT". Simple descriptions generated without the "Pseudo-Con Coo" input lack sufficient visual information, leading confusion and hallucination in the reasoning process of DeepSeek-R1. In contrast, detailed descriptions enhanced through our Modality Bridging with "Pseudo-CoT" integrate high-quality visual information into textual descriptions, which facilitates accurate reasoning and enables RI to generate correct answers.
fifth step in the final 50 steps of the RL training process, thereby avoiding the statistical bias caused by training instability.
To assess the quality of the Vision-R1- R1-cold dataset, we apply the same training hyper-parametter of the cold-start initialization to Llama-3.22 2-V-Inntrruc (Dubey et al. 2024] for SFT, resulting in another post-cold-stee MILM,, Vision-R1-Llama V-CI, which can also used for subsequent RI. training.
B COT COMPARISON FOR DATA CONSTRUCTION STRATEGY
We compare the CoT example generated by descriptions with and without "Pseudo-Co" in Fig. When directly the naive image description the input for DeepSeek- -11, the strong reasoning LLM cannot generate the correct trajectories for a massive amount of VQA data. This is because the MILM does know which image information is important for reasoning, and the generated image description lacks key converted image information, resulting in unusual CoT data.
C ADDITIONAL DATASET ILLUSTRATIONS
We provide some more examples of our Vision-Rl-col dataset in Fig. Fig. showcases multiple response instances generated by our Vision-RI model, including comprehensive reasoning processes and corresponding final answers.
D DATA SOURCES
Our proposed Vision-R Mision-RIccole 1-cold dataset consists of wide range of multimodal data, including the following categories:
Mathematical Data: GLLaVA (Gao et al. 2023). GEOS (Seo et al. 2015). UniGeo CCeen
al. 2022). GeoQA Plus (Chen al. 2021), Geo3K (Lu al. 2021a), Math Vision (Wang
17

---

Published as a conference paper at ICLR 2026
represented the wanibble" 'r' 1/and a height represented by 39
determise the lsteral surface using Cheions: A: the In de bi
ottilo let's The probier
the varidbles The that the is is L in p. The nadial 1 and siant hright is Hlaging those values mulliphy y 1- 3. which gives by. Looking at the choices, aption B is Bn. That motches the colculation. So the craner should be vihiiinesserr Finel Arowor& vlanvve
ichirol surfoce
Question: The dionsed 4BC) is characteriand by the side leegth 48 or 'n' usite : and the site lenght ACes 'x" unite in 8). Deternine the orea of the olomeed using given side lengths and lecate the poin of intrsection of the diggnels within the dionond. Cholonsis
otirlo Oily, I saed to find the oreo of the denped-dppee pendelettoal AGCO From the mage, it's rhombur becouse the chagonsis bisect each other right angles, forming four cong went right trinnges formals given Area 3/2
where and are lengtha
that
However, feow the side leigth AB B units Since the diagenals of rhumba bsset each ofher ot right ongles, each half of the diggenal forms leps of right tricngle with the side of the rhamis as the hycoterase. Let me voualiee this The diagnas mersect at point ahich disperll, which would
midsint of both digerals So (80/2)
8/2.
BD other
AO Since triengc AB
is
numbers 4" e 80 5 36 -0 25 80 9 50 - Tharefore, a0- 2 80 6 units New using the fermade: 1/2 Bb- 1/2*8* 8 5- 24, Wait bui 5% is 18, hall of That 24, But looking of the chiices is 24 But well, moybe made o erstake Let double- us BD-2"--66
indiude 16-9 yes okagma's are 8 und Anea is (866/2224 So is cornect But wall The prebien shows V Sammnn ABCD charscterized by the side length in unit (n-3) and the side longh AC es unit (n- Walt. noybe ssauddrstteed AC & deggonal not side Se the givn sice longth and daggnaa
Yes, is 8,
Se
(8"61/2
The ardwer droul 5. AD
walt the
rescaluuitte BDP Let's
uthisinnddeer Anowert Verswer
Choices A 8i6 0:12 bi 24
<thillo Okay, let's
there's a triongle Firsh, parpendcckar bisester dhiidea the tmargle ints hwo congruent intrgees, right?
perpend
Becouse a bioecter spiiss line into two equal parts Sa MK is the perendioser btaestor of JL, than 2M rus equal NL sis IM is ML should clso be 5. Therefans, 2 is IM ML.
12 units Looking of the choiess is 12. So the ardwer should be C. Let me dosble desble.chesk. The prob len shons MC perpend biseter, so
in dpeintl The congruent segments JM and both 5, 12 Yep.
Question
lagage??
<theolo Cleyy let' mee The quaat on making thare's i laggage in the image depicted The mage depicts shous that some people ane board, which makes they would have loggage. Se the onaver shedld
cornying lugpege,
from platfarm
./ttankoossverr Find Asswer yea rlomwarrs
Figure 6: Examples of our Vision-RI-cole data. It comprises abundant information obtained through our Modality Bridging method.
Questian sheen figune, keown that angle ang orgle& 55 than the degnee angle 4 A:100 8:15 c120"
-thiko Ohay, let see The prebien displps that angles 1,2, and are each 55 deprees. Arale 4 odjecent to cagle 3 and they farm inser pai pair edds up degrees.
deprees. engle 3 der cel thet ongle must be minus me colculate that 55
stgle
be
Questhor: shoun the figurs AC+ BC,
CAU than perinater friong's DOE
choices: Aden llfen citon. 3.9cn
the blsects angle CAB. de onyle CAD cood to angle DeB Since ABC is isuccelss the ongle ot is guiht into hwo ogail parts by AD New. there's is line DE porperdicaton to AS, neeting of E. The pervectter triangle DRE is what we need to find. The ardwor tho ces are 6 or 9 on The haae ponmettrr oppil
That
thes anowen is porperdicsor to AB Maybe there's preperty here where the perieater DBE equils ABl becousse of sove triangle congranne or samwthing. Since the image daploys daplyss the pertecter equd which is on. thes the orower is dfthavoersweer Finsl AnovercA virrwerr
but anything. problen displys
on
perineeter of triongle
should be
86, AD
Queettonn As shown in the figune, PA and PS one tregent to orcee G at and respectivnly If ongle Cn O5.0 then the degrns of ongle Piee Cheices
e50 bilod"
<tirlo Clay, let's ane. The probiem obout fongente cinde from external poiet FA and 18 are tongnnsttuching the crde A and The ongle ot ii 65 degeass, and we need to find the mesore of angle First I nemember that the two teegent ong nents from angle external paich a arcle squil in length Se equals property gemettry
tmangle PABl msrreaie trisngle with
but question about orgle which po
diploye
tongests
190 dagress vinus the messure of the centrol onge sabtunded the points cornact alfermatively anether to then doott that the angle betweon the tangerts kanje P9 suplenertory the reccure of the contral ongle AOB So if can Find the recoure of angle 4OB. trin orgle 40B.
engle AD8,
angle ADE9
peists
points cornact
cidle ongle the hand.
ACB
the
point is grasn as 65 dey-ees Since point C en the circs. ongle ACS en inporibed ongle. The moosure of on insorbedionn ongle is helf the mossare
its intercopted arc So ongle ACS intercepts are AB, which mecre AB ls 66 130 Thorefore, the centrol ongle AOB is 130 degnees. Thes, angle targenss (ongle 180 130
Lest ohtieloanneerr Find
ongle
ongle between
Question As shown in the figare, the plant specing (the hor torizanta cirfance between two edjocest trees) requined to 4.0 troes are plantic hilside alspe 0.75 the plant spocin cla requined 4.0 that the alope distance behween two odjesent frees il
67n
<thenlo Okay, let' see The groblen is doout finding the siope distance between heo odjcentt trees on silsdd The hanizontal d stance between harzzettal meters and depp the hilside
alope usally rise over run. Ss sipe 075 that means every vertical change over that 4 meters Wall. The skape o 0.75, vortical change (it''s call it for harianite run (f) 4 meters would be 0'75 So v-o 7.75
matere So the veriical difeernce maters Naww the dippe disance would the hygatnaise orgst trinn with ayrt(4"2 3"2) sp1ti 9) syr1(5)
horszeenl lag 5 mettere the enewer thould A:5m o'thrdoonnwerr Find Areaer A cionnver>
Pyttegerean
slops
Figure 7: More output examples of our
on Math Verse benchmark.
et al.
2025). GeoMverse Kazemi et al. 2023), Math V360K (Shi et al.,
2024). IconQA Lu
et al.
2021b), TabMWP (Lu et al. 2023b). CLEVR (Johnson et al. 2017). CLEVR-
Math
[Lindström & Abraham 2022). and Super-CLEVR (Li et al. 2023).
General QA Data: ShareGPT4V (Chn et al. 2024a). PISC [i et al.
2017) VQA-AS Antol
et al. 2015), A-OKVQA (Schwenk et al. 20022) TextVQA (Singh et al. 2019), Vizwiz Guu rari al. 2018), and VQA2.0 (Goyal al. (017)
18

---

Published as a conference paper at ICLR 2026
Science and Medical Data: From GeoQA+ (Cao & Xiao 2022). CLEVR-Math (iidsström
& Abraham 2022), TQA (Kembhavi et al., 2017). A12D (Kembhavi et al. 2016). Sci-
enceQA (Lu et al. 2022). VQA-RAD Lau et al. 2018 and PMC-VQA (Zhang al. 2023)
Figure Understanding Data: From DVQA (Kafle et al., 2018), Doc (Mathew al.
2021
FigureQA (Kahou et al., 2017). PlotQA (Methani et al. 2000), ChartQA (Masry et al.
2022
Inf (Mathew et al. 2022). MultiHiertt (Zhao et al. 2022). and LRV-Chart Liu
al.
2023
E
LLM USAGE
In this paper, have used LLM refine some sentences and improve the grammar, making the paper more academic.
19


Reviews:
[{"review_id": "9EIZfImjLT", "reviewer": "Reviewer_i3eD", "summary": "The paper introduces a new reasoning model Vision-R1. The authors recognize that RL alone struggles to activate complex reasoning in MLLMs due to insufficient multimodal data, so they propose a novel approach using Progressive Thinking Suppression Training along with RL to refine reasoning processes gradually.The paper also introduces a new 200K multimodal CoT dataset without human annotations to serve as the Cold-start Initialization of Vision-R1 models. Experiments shows Vision-R1 gain better results than previous models with similar size.", "questions": "1. Could the model be tested on additional reasoning tasks on other domains to assess its generalization capabilities? For example, MMMU and MMMU-pro dataset.\n2. Will the two-stage PTST increase the overall computational overheads? Some statistics about training devices and time may demonstrate the training process more clearly.\n3. In tab.5, a three stage PTST layout is included, but it adds a middle stage between original stage 1 and 2. I'd like to know whether adding an additional stage after origin stage 2, like 16K*4, can improve the model's final performance?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. This paper proposes a novel way to generate large dataset without human annotations, which is possible to become a scalable method for dataset creation.\n2. Sufficient ablation studies furtther demonstrate how the training parameters in PTST are chosen, and the experiments results shows the effectiveness of propoesed dataset and training strategy.", "weaknesses": "1. All the experiments are carried out on math-related dataset (e.g., MathVista, MathVerse). Cross-domain dataset could be included to show the model's power of generalization.\n2. While effective, the cold-start process might be limited by the quality and scale of the initial dataset.", "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": "DOZoIJDPX2", "reviewer": "Reviewer_RiY1", "summary": "This paper introduces Vision-R1, a multimodal large language model (MLLM) designed to enhance reasoning capabilities through reinforcement learning (RL). The model addresses key challenges in incentivizing complex reasoning processes in MLLMs, particularly when dealing with multimodal tasks. Vision-R1 utilizes a novel cold-start initialization approach and Progressive Thinking Suppression Training (PTST) to improve its reasoning abilities. The model outperforms other MLLMs in multimodal math reasoning benchmarks, even with smaller model sizes.", "questions": "Refer to Weakness.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Vision-R1 shows competitive results on challenging multimodal benchmarks, with a notable improvement in performance compared to existing models.", "weaknesses": "1. Novelty:\nWhile the idea of combining cold-start initialization with RL in MLLMs is presented as a novel approach, it lacks sufficient justification for why this method is fundamentally different from existing methods. The literature already includes several studies that employ RL for reasoning in LLMs (e.g., DeepSeek-R1, Qwen2.5-VL), and this paper does not convincingly demonstrate that the proposed approach significantly advances the field beyond these existing methods. The claim of novelty feels overstated given the incremental nature of the proposed solution.\n\n2. Lack of Comparative Depth:\nAlthough the paper compares Vision-R1 with several state-of-the-art (SoTA) models, the comparison lacks depth in terms of experimental conditions and detailed analyses. For instance, the paper does not provide comprehensive insights into the limitations of the existing approaches it claims to improve upon. A more detailed ablation study that explicitly examines the trade-offs between different design choices (e.g., cold-start initialization vs. direct RL) would strengthen the claims.\n\n3. Evaluation Metrics:\nThe performance improvement reported (such as a 6% average improvement) is promising but not compelling enough. The results are not sufficiently backed by real-world applications or a broader range of benchmarks that would demonstrate the practical utility of Vision-R1 in diverse settings. There should be more emphasis on generalizability and long-term robustness, particularly in multimodal settings.\n\n4. Over-Emphasis on Technical Complexity:\nThe method includes complex steps like Progressive Thinking Suppression and Modality Bridging, which are essential for improving performance. However, the paper does not sufficiently explore the potential practical difficulties in implementing these methods at scale. A clearer discussion of how these methods can be generalized or optimized for deployment in real-world scenarios is missing.", "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": "M7bcyZQBMN", "reviewer": "Reviewer_XezA", "summary": "This paper introduces a training paradigm aimed at enhancing the reasoning capabilities of Multimodal Large Language Models. The authors first construct a 200K multimodal CoT dataset, named Vision-R1-cold, without requiring manual human annotation. They then propose a Progressive Thinking Suppression Training strategy, which progressively relaxes the upper limit on reasoning length during RL training. The method demonstrates performance improvements on several multimodal math reasoning benchmarks, including MathVista and MathVerse.", "questions": "Same as the content in Weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper's \"Modality Bridging\" method is a strength. It uses an existing MLLM to transform visual information into detailed textual descriptions. This enables a powerful text-only model (DeepSeek-R1) to generate high-quality, human-like CoT for multimodal data. \n2. The models achieve competitive performance on benchmarks like MathVista.\n3. The paper is clearly written, and the figures generally present the concepts effectively.", "weaknesses": "1. The Vision-R1-cold dataset is constructed by filtering samples from various sources, including LLaVA-CoT and Mulberry. The paper does not provide a clear analysis to rule out potential data leakage or overlap between these source datasets and the evaluation benchmarks (e.g., MathVista, MathVerse). This lack of a contamination check casts some doubt on the true generalization performance.\n\n2. The definition of \"Pseudo-CoT\" is perplexing. The authors initially introduce it as a negative, \"formatted\" reasoning style lacking true cognition. However, they then use this \"Pseudo-CoT\" as a critical component in their own data pipeline (Figure 2) to generate descriptions. This leads to a critical missing ablation study: What is the performance if one simply uses the \"Pseudo-CoT\" data directly for cold-start initialization? The paper does not compare SFT with the \"Pseudo-CoT\" data. This experiment is essential to prove that the complex, DeepSeek-R1-generated CoT is superior and that the complex data pipeline is necessary. Finally, this paper has a coupling problem: The performance gain from the cold-start data might come from two sources: (1) the data source selection process or (2) the complex CoT style. The paper fails to decouple these effects.\n\n3. The proposed Progressive Thinking Suppression Training is presented as a new strategy, but its technical contribution is limited.\n\n4. The paper successfully identifies and names the \"Overthinking Optimization Problem\", but the analysis of its root cause is shallow. The authors state that after SFT on complex CoT, correct answers are concentrated in shorter sequences. Why does this happen? Is the model merely imitating the complex style of CoT without grasping the reasoning? Or is this problem caused by the data itself or the SFT training dynamic? Crucially, would this \"overthinking\" problem still occur if the (simpler) \"Pseudo-CoT\" data were used for cold-start instead? The paper fails to investigate these critical questions.\n\n5. The \"Modality Bridging\" pipeline (MLLM -> Caption -> DeepSeek-R1) has a potential fundamental flaw. An MLLM's ability to \"caption\" an image with perfect, fine-grained detail is limited. The authors' method attempts to guide this by feeding the MLLM a \"Pseudo-CoT\" that already contains reasoning steps. This process, which is inherently \"answer-directed,\" could introduce a bias or lead to a feedback loop of hallucinations, where the MLLM generates a caption that justifies the answer rather than describing the image objectively. The paper does not analyze or mitigate this risk.", "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": "FPWvjOiG8y", "reviewer": "Reviewer_Lj3M", "summary": "The paper introduces ​​Vision-R1​​, a method to enhance reasoning in ​​Multimodal Large Language Models (MLLMs)​​ by combining ​​cold-start initialization​​ with ​​Reinforcement Learning (RL)​​. \n\nThis paper proposes a pipeline to build high-quality multimodal CoT datasets without human annotation, using MLLMs and DeepSeek-R1 and uses this pipeline to build Vision-R1-cold (200K samples)​​. \n\n​​This paper proposed Progressive Thinking Suppression Training (PTST)​​ to mitigate ​​overthinking​​ in RL training by gradually increasing reasoning complexity and analyzes differences between direct RL training and the combined approach of coldstart initialization and RL training.", "questions": "Seeing weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "​1. ​High-quality dataset construction:​​ The ​​Vision-R1-cold dataset​​ leverages ​​modality bridging​​ to generate ​​human-like CoT reasoning​​ without manual annotation, addressing a critical bottleneck.\n\n\n2. ​​Strong performance:​​ Vision-R1-7B ​​matches or surpasses larger models​​ (e.g., Qwen2.5-VL-72B) on ​​MathVista, MathVerse, and MM-Math​​, demonstrating efficiency and scalability.", "weaknesses": "1. The novelty is limited. The article is more engineering-oriented, and the overall innovation of the work is incrimental.\n\n2. The argument for the PTST method's effectiveness in mitigating the Overthinking problem is insufficient. The paper uses the results of Vision-R1-Long to reveal the Overthinking problem and compares it with Vision-R1 (w/ PTST) to demonstrate that PTST alleviates the issue. However, the length limit for Vision-R1-Long is 16K, while Vision-R1 (w/ PTST) has limits of 4K and 8K in its two stages. If the length limit for Vision-R1-Long were 8K (or 4K), would the Overthinking problem still exist? How would the comparison with Vision-R1 (w/ PTST) look then?  \n\n3. The high-quality dataset in the Cold Start phase is one of the paper's key contributions, yet the necessity of Cold Start is not sufficiently justified. The proposed method consists of two phases (see Table 4): Cold Start and GRPO (w/ PTST). The paper does not seem to conduct ablation experiments on Cold Start. Specifically, in Table 4, Vision-R1-Zero performs better than Vision-R1-Long, which undergoes Cold Start in addition to Vision-R1-Zero. Does this imply that skipping Cold Start is better? I believe that conducting an experiment with GRPO (w/ PTST) (without Cold Start) and comparing it with the existing Vision-R1 would validate the necessity of Cold Start.  \n\n4. The paper claims that one of its contributions is analyzing the difference between direct RL training and the combined approach of Cold Start initialization and RL training. The former corresponds to the Vision-R1-Zero model in the paper, but the training configuration (methods, parameters, etc.) for Vision-R1-Zero is not detailed. Additionally, the paper compares Vision-R1-Zero with Vision-R1-CI, but their training data differs. I think a more meaningful comparison would involve training Vision-R1-Zero with the same data as Vision-R1-CI before contrasting them.  \n\n5. The ablation experiments for PTST are insufficient. PTST primarily involves two settings: gradually increasing the sequence length limit $L_s$ and gradually decreasing the sampling count $G_s$. However, the paper does not conduct ablation experiments for either setting. For example, why should the sampling count $G_s$ gradually decrease? What would happen if it remained constant or gradually increased?", "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)