Revisual-R1: Advancing Multimodal Reasoning From Optimized Cold Start to Staged Reinforcement Learning
Authors: Shuang Chen
Hangyu Guo
Zhaochen Su
Yafu Li
Jiacheng Chen
Yulun Wu
Weijie Wang
ZhiYuan Feng
Xiaoye Qu
Yu Cheng
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal Reasoning
Multimodal Reinforcement Learning
Primary area: foundation or frontier models, including LLMs
TL;DR: ReVisual-R1 with PAD and the GRAMMAR dataset sets new SOTA across major multimodal reasoning benchmarks.

Abstract: Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex textual tasks, many works attempt to incentivize similar capabilities in Multimodal Large Language Models (MLLMs) by directly applying reinforcement learning (RL). However, they still struggle to activate complex reasoning. 
In this paper, rather than examining multimodal RL in isolation, we delve into current training pipelines and identify three crucial phenomena: 1) Effective cold start initialization is critical for enhancing MLLM reasoning. Intriguingly, we find that initializing with carefully selected text data alone can lead to performance surpassing many recent multimodal reasoning models, even before multimodal RL.2) Standard GRPO applied to multimodal RL suffers from gradient stagnation, which degrades training stability and performance. 3) Subsequent text-only RL training, following the multimodal RL phase, further enhances multimodal reasoning.
This staged training approach effectively balances perceptual grounding and cognitive reasoning development. 
By incorporating the above insights and addressing multimodal RL issues, we introduce \textbf{ReVisual-R1}, achieving a new state-of-the-art among open-source 7B MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath, LogicVista, DynaMath, and challenging AIME2024 and AIME2025.

Published as a conference paper at ICLR 2026
REVISUAL-RI: ADVANCING MULTIMODAL REASON- ING FROM OPTIMIZED COLD START TO STAGED RE- INFORCEMENT LEARNING
Shuang Chen'; Hangyu Zhacheh Su Yafu Li Yulun Wu', Jiacheng Chen', Jiayu Chen?, Weijie Wang'. Zhiyuan Feng', Xiaoye Qu Yu Cheng
Zhejiang University 2 Fudan University 3 Soochow University
Shanghai Al Laboratory The Chinese University of Hong Kong
Code: Ccteer/ttprrsegtaaaoo.ooo//ttttt
Accuracy Comparison Across Open-Source Reasoning Models
100
OpeeVi.Tiinne-77
Eurcku Quca
XXRL
ThiaaLite//BM
VLAS Thanker QVC.2.5V
VL.Sethinker--11
ReMisual Ri (0urs)
Accuracy
528
4200
40
Madh Verse
Mataviston
Dyna Math
WoMath
LegieVista
AIME24
AIME25
GPOA
MATH500
Average
Figure 1: Overall performance across five multimodal reasoning benchmarks (Math MathVision, Dy- naMath, WeMath, and VigieVista) and four textual reasoning benchmarks (AIME24, AIME25, GPQA, and MATH500). Our ReVisual-R1 achieves hetter performance than existing works.
ABSTRACT
Inspired by the remarkable reasoning capabilities of Deepseek-RI complex tex- tual tasks, many works attempt to incentivize similar capabilities in Multimodal Large Language Models (MLLMs). However, they still struggle to activate com- plex reasoning. In this paper, rather than examining multimodal RL in isolation, we delve into current training pipelines and identify three crucial phenomena: 1) Effective cold start initialization is critical for enhancing MILM reasoning. In- triguingly, we observe that initializing with carefully selected text data alone can lead performance surpassing many recent multimodal reasoning models, even before multimodal RL. 2) Standard GRPO applied to multimodal RL suffers from gradient stagnation, which degrades both training stability and performance. 3) A final text-only RL tuning stage, conducted after the multimodal RL phase, is effective at further sharpening multimodal reasoning capabilities. Capitalizing on these insights, we introduce Re ReViual-R11, achieving a new state-of-the-ard among open-source 3B and 7B MILMMS on challenging benchmarks, including Math- Verse, Math Vision, WeMath, LogicVista, DynaMath, and challenging AIME2024 and AIME2025, This paradigm demonstrates robust scalability, proving effective at both 7B and 3B scales. Our findings chart new course for training power- ful multimodal reasoners, demonstrating that a carefully orchestrated, multi-stage strategy is key unlocking their full potential.
Equual contributions. Corresponding authors.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recently, the field of large language models (LLMs) has witnessed significant advancements in complex cognitive reasoning (Zeng et al.; Yan et al., 2025; Zhang et al., 2025a), notably exemplified by reasoning models like DeepSeek-R1 (Guo et al., 2025a). These models successfully leveraged Reinforcement Learning (RL) to facilitate the self-emergence of intricate reasoning abilities in text- only models. A natural and ambitious next step is to extend this RL paradigm to Multimodal Large Language Models (MLLMs), with the goal of unlocking a similar lapp in multimodal cognitive abilities (Huang et al., 2025b; Meng et al., 2025b; Xia et al.; Peng et al., 2025).
However, this ambition has been met with a formidable challenge. The direct application of text- centric RL techniques to MLLMs has yielded diminishing returns, suggesting that the path to mul- timodal reasoning is not simple matter of architectural extension. A fundamental question arises: how does one cultivate the abstract linguistic skill while simultaneously grounding it in the continu- ous, -ige-dimeniioaal space of visual perception? Naive co-training often results in compromise, where neither modality's 's potential is fully realized. Motivated by this critical impasse, we undertake a rigorous dissection of the MLLM training pipeline. Our investigation uncovers three foundational insights that, when addressed in concert, chart new and more effective course for developing pow- erful multimodal reasoners.
First, observe that sufficient cold start initialization is indispensable for effectively cultivating the reasoning ability of MLLMs. Conventional cold-start phases for MLLMs often rely on simplistic vi- sual and textual pre-training corpora (Yang et al., 2025; Huang et al., 2025b; Wang et al., 2025; Deng et al., 2025; Chen et al., 2025b; Feng et al., 2025). This initial deficit critically hinders the subsequent RL stages from eliciting sophisticated, self-critical reasoning patterns. To unlock deeper delibera- tive reasoning in MLLMs, an enriched cold-start initialization is therefore not merely beneficial but indispensable. Specifically, initializing with carefully selected text data that instills foundational reflective capabilities and the capacity for extended Chain of Thought (CoT) reasoning proves to be a powerful strategy. Intriguingly, such targeted textual initialization allows our model to surpass the multimodal reasoning performance of many recent multimodal reasoning models.
Second, we identify that the standard Group Relative Policy Optimization (GRPO) algorithm (Shao et al., 2024), commonly applied for multimodal RL, suffers from gradient stagnation problem. This issue significantly degrades both the training stability and the ultimate performance of the multi- modal RL phase. To address this fundamental limitation and improve the efficacy of multimodal RL, we propose Prioritized Advantage Distillation (PAD). PAD is designed to mitigate gradient stagna- tion by strategically filtering out zero-ad advatagge samples and re-weighting informative trajectories, thereby focusing the learning process on more impactful data and improving training stability.
Third, we discover that conducting further post-training using text RL after the multimodal RL training phase can further enhance multimodal reasoning ability. This stage acts as a polishing step, sharpening the model's linguistic expression and logical consistency without eroding the newly acquired visual grounding. It consolidates the model's capabilities, leading to a synergistic whole greater than the sum of its parts.
Synthesizing these insights, we propose principled, three-stage training curriculum that strategi- cally sequences these learning phases: (1) text-centric cold-start forge powerful reasoning engine, (2) a multimodal RL phase with PAD to ground this engine in vision, and (3) text-only RL refinement to consolidate and polish the integrated skills. The culmination of this methodology is Re Risuall-R,, the first 7B 1B-parameter open-source MLLM architected around this curriculum. Exten- sive experiments on a suite of challenging benchmarks, including Math Verse (Zhang et al., 2024b), Math (Wang et al., 2024a), Math Vista (Lu et al., 2023a). DynaMath (Zou et al., 2025), We- Vista (Xiao et al., 2024), as well as the AIME24/25 (Li et al., 2024), GPQA (Rein al., 2024), MATH-500 (Hendrycks et al., 2021) benchmark, confirm that ReVisual-RI significantly outperforms much larger public models. We further validate the scalabil- ity of our framework by demonstrating its effectiveness at the 3B model scale.
Math (Qiao et al., 2024a), and Vista
To summarize, our contributions are as follows:
We challenge the conventional MLLM training paradigm by demonstrating that text-centric, high-difficulty cold-start is the crucial, and previously overlooked, foundation for unlocking elite multimodal reasoning.
byradigmby
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We identify the fundamental problem of gradient stagnation in multimodal RL and propose Pri- oritized Advantage Distillation (PAD), a novel and effective solution that stabilizes training and enhances sample efficiency.
We present ReVisual-R1, an open-source 7B MILM developed through principled three-stage curriculum. This approach uniquely cultivates deep, self-reflective reasoning and robust visual grounding, enabling VeVisall-Ri to achieve state-of-the-art performance on complex multimodal reasoning tasks, rivaling even larger proprietary models.
PRELIMINARIES
In this section, we first formulate the task setting and key concepts in the multimodal reasoning problem. Then, we describe the base training algorithm framework used in our method.
2.1 MULTIMODAL REASONING FORMULATION
In multimodal reasoning tasks, input can be represented as x = (v,4), where v denotes the visual content, and q denotes the textual query. Our work aims to guide MLLM to generate mullii-tepp. self-reflcctive reasoning process f, which ultimately assists the model in producing a solution y that correctly answers the query based the multimodal input.
Formally, we aim to learn a policy (b(2))) parameterized by 8, which maps the input question space A' to the solution space y. Our objective is to optimize the model parameters such that the expected reward r(y, x) over the output distribution is maximized:
f" arg max Rey x)]
8
where represents the distribution of multimodal reasoning tasks. Similar to Deepseek R1 (Guo et al., 2025a), we mainly use rule-based reward, r(x, v) = 1 if is correct, otherwise r(z, v) = 0.
2.2 GROUP RELATIVE POLICY OPTIMIZATION
Group Relative Policy Optimization (GRPO) extends traditional policy optimization methods by organizing training samples into groups and optimizing policies relative to reference models within each group, offering several advantages for training language models on complex reasoning tasks.
Formally, given batch of samples B, GRPO divides them into K groups {G1, G2, ....... GK based on certain criteria. For each group Gr we maintain both policy model my and a reference model Th. The GRPO objective for each group is formulated as:
The (y((r) Te (y
<o(y)<r) Tea (21(²)
( A(z, (().))
En,. Byy-To(ll))
min
A(z, v), clip
(2)
where is hyperparameter controlling the size of the trust region, and A(x, 8) is the group-specific advantage function. For each input with Gi generated responses (y1....,Be yG within group, the advantage for response y; is defined as:
Á(x, vi) r(I,3:)
((x,8c)))
std((r(c, vi). (G)))) +
where e is a small constant for numerical stability. This relative advantage is then used within clipped surrogate objective function. r(z, v1) represents the reward for response ys to input This advantage function measures how much better a specific response compared to the average performance within its group, normalized by the group's reward variance.
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Data Collection
Curation
Text Only
NV- EV-Embedcing
Coldstort
MM-RL
+
Text-RL
Reasoning
Model
HDBSCAN
Overview: ColdStart and Staged Reinforcement Optimization
D
Prioritized Adventage Distillation
O₃
Reword Group Computatioo
Aa
7
Distilled Updated
Policy Model
Dy
Toop Piorit aed D still Botch
Ox-1
weighT
-0
Butch
Illustration PAD mechanism algorithm
Figure 2: (Top): the overview of our proposed Re Resualll-RI framework. After collecting and curating data, VeViulll-RI contains cold start and staged reinforcement learning. (Bottom): the process of our proposed prioritized advantage distillation (PAD) for multimodal reinforcement learning.
OPTIMIZED COLD START FOR MULTIMODAL REASONING
this section, we first show an intriguing finding in the cold start of multimodal reasoning in Section
3.1, paving the way for the strategy of our data curation pipeline in Section 3.2. As shown in Figure
2, our Revisual-RI includes a cold start stage followed by staged reinforcement learning.
3.1 PRELIMINARY STUDY OF COLD START
30
To investigate the effectiveness of cold-start training (Guo et al., 2025a), we first collect
Absolute Performance Improvement
-----
MM Coldd Start Avg.
Cold
25
two open-source cold-start multimodal datasets, Vision-R1 (Huang et al., 2025b) and R1-One- Vision (Yang et al., 2025), along with two cold-start textual datasets, DeepMath (He et al., 2025) and OpenRI -Math (Face, 2025). Then
Matt Visioe
353
ALME2024
260
20
MATH500
16.4
12.9
we randomly sample 40,000 instances from these datasets to fine-tune Qwen2.5-VL-7BB
10
Instruct (Bai et al., 2025). The fine-tuned mod- els are subsequently evaluated on multimodal reasoning benchmarks (Math Verse and Math Vi- sion) as well as textual reasoning benchmarks (AIME24 and Math500). The experimental outcomes and average performance enhance- ments from the multimodal and textual cold- start datasets are illustrated in Figure 4.
Vision-RI
Rl-Oce-Vasion Models
Delp
OpenR1-Mall
Figure Absolute performance improvement on ----.---LL-B--nsttuee across textual and multi- modal reasoning tasks. The purple and red dashed lines represent the average absolute gains VisionR1/RI VisiVision and DeepMath/OpenRl --Math over the baseline, respectively, across four reasoning tasks.
The results in Figure 4 reveal that models trained with text-only cold start data exhibit substantial improvements
both textual and multimodal reasoning tasks. In contrast, mod- els trained solely on multimodal datasets, such as Vision-R and RI-One- 1-OOne-Vision, show limited gains in both multimodal and textual reasoning. This suggests that the complexity and patterns presented by textual cold start data may better stimulate the models' reasoning capabilities.
To further investigate this observation, perform analysis using subset of 100 examples pled from the Vision-RI (Huang et al., 2025b) and DeepMath (He et al., 2025) datasets. Specifically, analyze the response lengths and pass rates of the doubao- thhinking-pro-viiii model (Seed
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Table 1: Textual and multimodal reasoning datasets source of our GRAMMAR.
Multimodal
Textonty
Source
Sumples SSuuree
Sumples
Sumples
Seurre
Samples
FAgunOA
Super-CLEVR
Mall- (Altslak
251K
GAIR, LIMO cial.
0.58
MAVIS (2huse 20256)
TabMW? 20236)
Mall- RL.U
alK.11 (Mfcenishhff
GeeQA (Ction al. 2023)
SK
Unitino Chn etall 2003)
16%
Opent Cleam, 2005)
114%
OpennMatii (Moshirn 2025)
3,300
Goonesy (Le at, 2021a1
2.KK
MilliMat (Peng at. 2024
2008
Desphaath (Hl ot 2025)
103K
OccaMlut (Mitaa et 20246)
2008
InmQA (1 at, 2001189
100K
OpenRt 1-276 (Fras,, ((aa)
2003)
130K
Numoukhitt-Odl
20143
KSVK
et al., 2025) on these samples. Responses to textual prompts from DeepMath averaged 8,207.76
tokens, which is substantially longer than the 821.48 tokens generated in response to multimodal prompts from Vision-R1. Moreover, the pass rate for Vision-RI is 96.00%, whereas Deep.atth achieves pass rate of only 75.0%. These findings further indicate that current multimodal cold start datasets may lack sufficient complexity to inspire advanced reasoning capabilities of reasoning models. Therefore, in this paper, we adopt textual-only data for the cold start stage.
3.2 GRAMMAR: GENERALIZED MULTIMODAL REASONING DATASET
Informed by Section 3.1, in this paper, we develop the Generalized Multimodal Reasoning Dataset (GRAMMAR) to enhance the generalization of reasoning capabilities in multimodal models. Specif- ically, the GRAMMAR dataset consists two components. For the cold-start stage, includes 283K diverse and complex textual samples that feature explicit reasoning paths. For reinforcement learning with a verifiable reward, it contains an additional 31K complex textual examples and 21K multimodal questions, all of which are annotated with ground truths.
As shown in Figure 2, the construction of GRAMMAR involves multi-stage curation pipeline. We begin by amassing open-source reasoning datasets in Table spanning various difficulty levels. This initial collection underwent rule-based filtering to ensure answer verifiability, excluding items like proof problems and those with difficult-to-verit verrify ground truths. Subsequently, Qwen2.5-VL- 7B-Instruct is employed for initial pruning of overly simple or complex questions. Then, Qwen2.5- VL-32B IIIstrctt is used to assess the remaining samples to classify them into ten difficulty levels. To maximize data diversity and minimize redundancy, we encoded questions using NV-Embedding- V2 (Lee et al., 2024), applied HDBSCAN (Campello et al., 2013) for clustering, assigned topics to clusters via Qwen2.5-78B IB-nntrrttt,, and performed balanced sampling across both topics and difficulty strata.
4 STAGED REINFORCEMENT OPTIMIZATION (SRO)
In Section 3, data investigations and the curation of the GRAMMAR dataset highlight the ne- cessity of high-quality, reasoning-focuus data for developing advanced MLLM capabilities. this section, introduce Staged Reinforcement Optimization (SRO) to systematically cultivate robust reasoning and diverse competencies in MILMMs. Specifically, SRO contains two stages including multimodal RL and subsequently textual RL.
4.1 STAGE 1: MULTIMODAL RL
After the cold start training, the SRO framework commences with a dedicated Multimodal Rein- forcement Learning (MRL) phase. This initial stage is pivotal for enabling the MLLM to ground textual concepts in visual information and execute cross-modal reasoning, primarily using the multi- modal samples from our GRAMMAR dataset. We employ GRPO the core RL algorithm for this phase. To ensure stable and effective learning, particularly when dealing with complex tasks and po- tentially sparse rewards common in multimodal settings, we propose novel Prioritized Advantage Distillation (PAD) to improve gradient quality by addressing specific GRPO limitations.
4.1.1 PRIORITIZED ADVANTAGE DISTILLATION (PAD)
During multimodal RL training, we discover significant challenge when applying GRPO in com- plex multimodal settings is "Gradient Stagnation". This phenomenon refers to reduction in learn- ing efficacy due to a predominance of near-zero advantage estimates, which particularly acute
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when dealing with sparse binary rewards. Essentially, if entire groups of generated responses yield uniform rewards (e.g., all correct or all incorrect), the resulting advantage signals become null, lead- ing to zero policy gradients and thereby halting learning for those samples. This issue, also noted in concurrent works (Wang et al., 2025; Yu et al., 2025), can severely impede training progress. To specifically counteract gradient stagnation and enhance the efficiency of GRPO, introduce Prioritized Advantage Distillation (PAD). PAD refines the training process by strategically focusing updates on the most informative samples within each batch, namely those exhibiting significant, non- zero advantage signals. The PAD mechanism, detailed below, operates on each batch after initial advantage estimation. mainly contains the following three parts:
Per-Sequence Advantage Calculation: Compute the absolute advantage |Λ,] for each sequence i in the original batch B, representing its learning signal magnitude.
Effective Sample Filtering: Form an "effective set" & by selecting sequences i whose absolute advantage |A,] falls within a specified informative range (Tlow:Thsghl Thigh] Critically, Tlow > filters out stagnant (near-zero advantage) samples, ensuring that candidates for sub-sampling provide potentially useful learning signals.
Prioritized Sub-sampling from Effective Set: From this effective set E, k min(p|B], [E]) sequences are drawn to form a distilled mini-batch. Selection is prioritized based on sequences' absolute advantages (A, for E &), with the probability for selecting sequence i determined by lemperature-controlee Softmax distribution:
exp(A./T)
Pr(i is selected
EE Ejec exp(A,/T)
The temperature 7 governs sampling concentration and typically decayed during training (e.g.. linearly from 1.0 to 0.3) shift from exploration towards exploitation. This enriches the mini- batch with the most informative samples from
PAD thus directly counteracts gradient stagnation via a dual mechanism: first, by filtering out stag- nant samples, and second, by prioritizing updates using informative, non-zero advantages from the remaining set. This selective optimization of the learning process ensures efficient computational resource allocation towards high-value samples. Consequently, PAD leads to enhanced training sta- bility, improved learning efficiency, and more effective acquisition of complex reasoning skills.
4.2 STAGE TEXTUAL RL
While MRI. is indispensable for grounding reasoning across visual and textual inputs, intensive MRI. training can inadvertently lead to a decline purely textual capabilities. To further elevate the model's capacity for sophisticated abstract reasoning, ment Learning (TRL) phase. This stage aims to achieve both robust linguistic fluency and advanced reasoning. Linguistic fluency is restored and enhanced by fine-tuning high-quality, text-only corpora focused on iestructiionfolllvviii
integrate a subsequent Textual Reinforce-
and conversational abilities. Simultaneously, to foster ad- vanced reasoning. the TRL phase exposes the model complex, text-centric problem-solving tasks. This compels the model to refine and generalize intricate reasoning patterns, articulate multi-step thought processes with greater clarity, and master linguistic nuances essential for higher-order cog- nition. For policy optimization during this TRL phase, we employ GRPO, augmented with our proposed PAD mechanism for efficient sample utilization.
EXPERIMENTS
5.1 EXPERIMENTS SETUP
Benchmarks To comprehensively evaluate our model's performance, we selected diverse suite of benchmarks targeting a wide range reasoning abilities. The evaluation suite organized by domain: (1) Multimodal mathematical reasoning, assessed using MathVerse (Zhang et al., 2024b), Vision (Wang et al., 2024a), Math Vista (Lu et al., 2023a), DynaMath (Zou et al., 2025), and WeMath (Qiao et al., 2024b); (2) General multimodal reasoning, evaluated with LogieVista (Xiao et al., 2024), MMMU (Yue et al., 2024a), MMMU-Pro (Yue et al., 2024b), CMMMU (Zhang et al., 2024a), and MMStar Chen et al. (2024); and (3) Text-based reasoning, measured challenging

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mathematical benchmarks like AIME24/25 (Li et al., 2024) and MATH-500 (Hendrycks et al., 2021), as well as general reasoning benchmarks such as GPQA (Rein et al., 2024) and MMLU Pro (Wang et al., 2024b). Performance is reported as pass @ accuracy for all benchmarks, with the exception of AIME24/25, for which we use average 32 accuracy.
Baselines As shown in Table 2, baselines include: (I) leading closed-source models doubao- ussionprro322K (Guo et al., 2025b), OpenAI-GPT-40 (Hurst et al., 2024), Souude-3.7-oomee (An- thropic, 2024), Gemini-2.0-Flast (Gemini Team et al., 2023). (2) open-source general-purposs MILMM Qwen2.5-VI. VL--B (Bai et al., 2025); and 3) specialized open-source reasoning MILMMs VLAA--hhnnerr-77 7B (Chen et al., 2025a), D..Tnniiii (Deng al., 2025), AMRI-Math-vO (Leng et al., 2025), MM-Eureka (Meng et al., 2025a), and VL-Rethinker-7B (Wang et al., 2025).
5.2
IMPLEMENTATION DETAILS
Our Re Visuall-R1 models are based on the Qwenn2255VII-7B-nnnntr and 110--..5-VLL33B--mme models. Its training comprised three distinct stages. The process begins with cold-start phase utilizing LLaMA Factory (Zheng et al., 2024) and pure text data to establish foundational language understanding, Following this, Multimodal Reinforcement Learning (MRL) implemented using Easy RI (Zheng et al., 2025). In this stage, the GRPO Kullback-Leibler (KL) divergence constraint omitted encourage broader policy exploration. The final stage TRL also conducted via Easy R1. During TRL, the vision tower is frozen to concentrate learning on textual reasoning, and small KL penalty is incorporated alongside entropy annealing to enhance training stability. All experiments are conducted on setup of 8 NVIDIA A A100-80G GPUs. Detailed prompt settings and training hyperparameters are provided in the Appendix.
5.3 TRAINING DATASETS
The training of Re Risuall-RR follows our proposed three-stage methodology, utilizing carefully cu- rated datasets for each phase. The cold-start phase employed approximately 283k pure text entries focused on establishing foundational language understanding. Subsequently, the Multimodal Re- inforcement Learning (MRL) phase used approximately 26k diverse multimodal entries from our GRAMMAR dataset to develop multimodal reasoning. Finally, the TRL phase consists of approxi- mately 30k text entries designed to refine nuanced understanding and generation capabilities.
5.4 MAIN RESULTS
As shown in Table 2, our model demonstrates superior performance on math-related benchmarks compared to other open-source reasoning models and even outperforms some commercial MLLMs.
Specifically, on the 7B model scale, ReVisual-R R1-7B achieves an impressive average score of 53.1 %, significant improvement of +16.8 percentage points over the baselines in Tabel 2 textual and multimodal benchmarks. Notably, Re Visuall- secures the top position among open-source con- tenders in nine out of ten individual benchmarks: Verse (+5.4% A), Math Vision (+18.6% A), DynaMath (+4.5% A), WeMath (+0.2% A), Vista (+1.5% A), AIME24 (+43.3% A), AIME25 (+15.4% A) GPQA 10.1%% A), and MATH500 (+22.0% .00 A). All these results present the superior performance of our Revisual-RI on both multimodal benchmarks as well as textual benchmarks.
When compared closed-source commercial models, Re ReVislll-RI I also exhibits highly competitive performance, For instance, its average score (53.1 1%) surpasses that of OpenAO--PT--aaan (41.6%). On specific demanding benchmarks such as MATH500, Re Visual-RR (89.2%) outperforms both doubao- 33-vision-pro-222 (85.2%) and OpenAI-GPT-40 (74.6%). Similarly, on AIME24 and AIME25, Re ReVisual-R1 demonstrates substantial leads over these commercial offerings. While some closed- source models like doubao- 1.5taao--.--isiii ...--iiioon-po-3222. show a higher overall average (55.8%), Visusl RI's ability to outperform them on several key reasoning tasks highlights its specialized strengths.
To further demonstrate the effectiveness of our training framework, we conduct experiments on 3B model scale, leading to Dovisual-RR-333 As demonstrated in Table 2, our model also demonstrates superior performance across all benchmarks and obtains an average 16.0% improvement. These results further verify the generalization of our Revisual-R1.
this model size. Specifically, model outperforms L.AA-Thinker-3B
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Table Performance comparison diverse benchmarks. The best underlined (among open-source models). Scores italics indicate that they are not reported in original work and are obtained using the VLMEvalKit (Duan et al., 2024) for evaluation. AIME24 and AIME25 results are averaged over eight independent inference runs to reduce score variance. Math Verse-V, DynaMath and WeMath-S denotes the vision-only, worst, and strict settings, respectively. A (c.g., Ours-Open 3B Best) denotes the improvement margin of the corresponding ReVisual-RI model over the best-performing open- source baseline model the same scale across respective column.
bold: the second best
Multimodal Reasoning Benchmarks
Textual Reasoning Benchmarks
Model
Verse- MathVisson Vath DynaMath-W WeMath-S LogicVista AIMEM AIME25 GPQA MATH1500 Avp.
CiasedSource
doabao- S--iiioo-prr pro-32k
64.7
51.5
78.6
44.9
64.2
65.7
26.7
20.0
56,1
85.2
5588
OpenAIGPTHo
40.6
31.1
59.9
34.5
42.9
64.4
9.3
49.9
74,6
41.6
Claude3. 7Soanes
52.0
66.3
29.7
20.0
61.1
482
Gemini2.000 OFFlss
43.6
47.8
70.4
42.1
47.4
52.3
33.3
36.7
35.4
69.0
47.8
3R-cale MISMs
Qw
6.7
25%
18.2
38.5
3.1
Vismal-R1-38
46.5
(Ours-Open
Models
Qwen2.5VL.7B
26.6
68.2
12.6
24.5
35.6
10.0
32.8
67.2
32.3
OpenVLThinkerID
38.1
25.9
72.3
16.8
33.2
44.5
5.0
28.3
51.0
31.9
MM-Farcla-Quen-78
23.0
PhinkL.ne7BVT
24.6
71.6
16.5
42.7
8.8
27.9
36.3
VLA.A-hhinkm--T
48.2
26.4
22,4
41.5
48.5
0.8
12.6
30,8
30.8
33.0
R--Rehinker-71
46.4
28.4
73.7
17.8
36.3
42.7
2.9
27.4
47.0
33.6
ReVisall-R1-78
23.1
(Ours-Open 7B Rest)
+10.1
Table 3: Ablation study of different training stage combinations applied to the ReVisual-R1 model, huilding upon Cold Start. Best results per column are bold and second-best are underlined. Mixed-RI. denotes that the model jointly optimized with both MRL and TRL objectives in mixed training stage.
Training Strategies
Math Verse-V
MathVision
Math
DynuMath-W
WeMath-S
Logic Vista
Avg
Cold Start (CS) only
51.9
47.9
70.5
26.5
35.8
50.1
47.1
MRL
50.9
47.6
71.9
25.7
38.8
$1.2
47.7
CS TRL
47.3
47.3
71.0
25.2
33.7
44.7
44.9
CS MRL++ TRL
53.6
48.8
73.1
27.5
42.0
52.3
49.6
CS TRL MRL
47.5
48.0
70.3
24.2
35.0
48.2
45.5
CS Mixed-RL
49.3
48.2
72.1
25.7
38.8
51.2
47.6
Collectively, these results validate the efficacy of our proposed training method, including the struc- tured three-stage curriculum and enhancements like Prioritized Advantage Distillation.
5.5
ABLATION STUDY
5.5.1
ABLATION ON SRO
To validate the effectiveness of our Staged Reinforcement Optimization (SRO) framework, we con- duct ablation studies on different combinations of Multimodal RL (MRL) and Textual RL (TRL) phases, all building upon optimized text-centric cold-start (CS). As shown in Table 3, the empir- ical results verify that our proposed CS + MRL + TRL (Re Visualu R1-MTR) sequence consistently yields the highest average performance (49.6 Avg). This outcome affirms core hypothesis: an initial MRL phase establishes strong visual grounding, followed by TRL phase to refine textual fluency and abstract reasoning, is crucial for developing superior multimodal capabilities.
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Table 4: Ablation results demonstrating the impact of Prioritized Advantage Distillation (PAD) and its core components. Best results per column are bold and second-bes are underlined.
Model Configuration
Strategy
Math Verse
Math Vision
DynaMath Math
WeMath LogicVista
Avg
ReVisual-R (CS MRI.)
PAD
50.9
47.6
71,9
25.7
38.8
51.2
47.7
Components:
Full PAD (Baseline)
GRPO-Ruseline
47.6
45.8
68.8
25.2
34.8
48.6
45.1
No Prioritized Sub-sampling
GRPO-Fitter
47.7
46.7
71.2
25.5
35.1
49.7
46.0
No Effective Sample Filtering
Random Sampling
47.9
46.4
70.7
26.1
37.1
49.3
46.2
Other Strategy
DAPO
48.3
46.3
69.2
25.4
38.3
49.2
46.1
In more detailed analysis, the CS + MRL only model (47.7 Avg), while performing well on visually intensive tasks such Math Vista (71.9), does not reach the overall performance of the full MTR sequence. This further highlights the importance of the subsequent TRL. stage. The alternative SRO ordering, CS + TRL + MRL (45.5 Avg), also proved less effective than our MTR approach. This finding indicates that establishing strong visual grounding before intensive textual refinement allows for more synergistic learning.
In addition, we perform a mixed- RL strategy to investigate the necessity of our staged reinforcement learning this setting, we train the cold-start model a mixture of multimodal and textual data. However, as shown in Table 3, we observe that this mixed setting can only get an average 47.6 score, which significantly lower than the performance of our staged RI. It shows that our MRL-then- TRL ordering within our SRO framework is more effective strategy than simultaneously training on mixedd-modalle data in the RLVR stage.
5.5.2 ABLATION STUDY ON PAD
In this section, we conduct ablation studies to verify the effectiveness of our proposed Prioritized Advantage Distillation (PAD), examining its overall efficacy, the contribution of its components, and its sensitivity to key hyperparameters.
To assess PAD's impact, its full implementation is compared against GRPO-Baseline, GRPO-Filte- only, and Bandom-Sampling strategies. Table 4 demonstrates that full PAD achieved superior perfor- mance on mathematical reasoning benchmarks, highlighting the importance of its core components: effective sample filtering and prioritized sub-sampling. Mcanwhile, we compare our method with DAPO and observe that our PAD can also present significantly better performance than DAPO. To further demonstrate the effectiveness of our Training dynamics (Figure ??) further corroborate PAD's effectiveness, with its sampling strategy yielding higher reward accuracy and faster conver- gence.
5.5.3 GENERALIZATION OF REVISUAL-RI
Reward/Accuracy Comparison
To further evaluate the generalization capac- itt of our method, we test the model on sev- eral knowledge-intensive benchmarks, includ- ing MMMU, MMMU-Pro, and CMMMU, as well as on general-purpor multimodal per- ception (MMStar) and textual understanding (MMLU). The results, presented in Figure 5, show that our method consistently performs su-
0.40
PAD Sampling
Random Sampling
Reward/Acccracy
0.35
GRPO Filter
GRPO Baseline
0.30
0.25
perior across these benchmarks. The more de- tailed performance is shown in Table 6. It further indicates that the generality of our ap- proach general tasks and provides further evidence that textual reasoning capabilities can benefit general multimodal and textual tasks.
50
100
150
200
250
300
Training Step
Figure Ablation of training dynamics our PAD.

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Multimodal Large Language Model (MLLM) is key research area. While leading closed-source models (e.g.. GPT-o3 (OpenAI, 2025), Kimi-VL (Kimi Team, 2025)) excel at long Chain-of- Published as a conference paper at ICLR 2026
6 RELATED WORK
6.1 MULTIMODAL LARGE LANGUAGE MODEL
Thought (CoT) reasoning. open-source contributions have focused on CoT adaptations (Liu al., 2023; Guo et al., 2024; Su et al., 2024) and Supervised Fine-Tuning (SFT) with reasoning traces (Mitra et al., 2024b; Zhang et al., 2024c) (Gao et al., 2024). DeepSeek-RI (Guo et al., 2025a) has further spurred Reinforcement Learning (RL) applications for visual reasoning (Huang al., 2025b) (Dong et al., 2024) and specialized domains like mathematical reasoning. Nevertheless, many MLLM reasoning models (Wang et al., 2025) (Deng al., 2025) (Yang et al., 2025) (Chen et al., 2025b) (Huang et al., 2025a) are limited by generating relatively short responses, which often curtail genuine reflection, thorough visual exploration, and consequently, deep multimodal reason- ing. Our work, in contrast, introduces a novel framework to enable MLLMs to generate significantly longer, reflective responses, thereby facilitating long CoT reasoning to unlock more comprehensive multimodal reasoning capabilities.
6.2 REINFORCEMENT LEARNING IN REASONING
Current LLM research explores direct RL fine-tuning, specialized cold-start datasets for long-form reasoning, and advanced algorithms like Group Relative Policy Optimization (GRPO) (Shao et al., 2024) and its refinements (e.g.. DAPO (Yu et al., 2025), DR.GRPO (Liu et al., 2025), GPG (Chu et al.. 2025), AEPO (Chen et al., 2025c)) to elicit deeper reasoning. However, RL application to multimodal reasoning in MILMs nascent. Initial MILMs efforts focus on subdomains like math reasoning (Meng et al., 2025b; Peng al., 2025) generative reward models (Gao al., 2025), often utilizing data from commercial models. Nonetheless, successes such as DeepSeck-RR Ree' (Guo al., 2025a) rule-based RL are spurring similar MLLMs investigations, indicating growing interest in RL for unlocking sophisticated multimodal reasoning.
6.3 LIMITATIONS AND FUTURE WORK
Despite strong empirical results, ReVisual-RI has several limitations. First, it lacks a solid thcoret- ical foundation explaining why text-centric optimization strategiessuch optimized cold start and staged multimoda/ttex-omn/ RLeffectively improve reasoning. Second, scalability has only been validated mid-sized models (3B and 7B), leaving uncertainty about performance larger archi- tectures like MoE models or other model families. Finally, the interaction between data type and training stage remains underexplored. Future work will systematically study the ratio and quality of multimodal versus textual data during cold start and RL phases to develop an optimal curriculum that maximizes performance across STEM reasoning, perceptual grounding, and general tasks.
CONCLUSION
This paper introduces Re Resuual--R, 3B and 7B open-source MLLM designed to address prevalent challenges in cultivating sophisticated multimodal reasoning. By systematically integrating strate- gic, high-diffcully text-only cold-start phase for foundational reasoning. a Multimodal RL stage employing GRPO stabilized by our novel Prioritized Advantage Distillation (PAD) mechanism, and a final TextRL refinement phase, our structured three-stage curriculum demonstrates that thought- ful data strategy and targeted algorithmic optimizations are pivotal. Re ReVisual-RI achieves superior performance among open-source 7B models on a suite of challenging multimodal, textual reasoning and generalization benchmarks. This work underscores that careful curriculum design and algo- rithmic enhancements, rather than sheer model scale, can unlock robust, self-reflective multimodal reasoning.
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REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we provide an anonymous code repository in the supple- mentary materials containing the full implementation of Revisual-RI algorithm. All datasets used for training and evaluation are publicly available, and a comprehensive breakdown of our ex- perimental setup, including all key hyperparameters, is detailed in Appendix.
ETHICS STATEMENT
The development of our model, Re ReVisual-RL -L, and the curation of the GRAMMAR dataset were conducted with close attention to ethical guidelines. Our research grounded in the use of publicly available and open-source academic datasets, which were rigorously filtered during the creation of GRAMMAR to ensure no personal, private, or sensitive information was included. The work does involve human subjects, and its objective is to advance research in multimodal reasoning for the open-source community, We foresee no direct societal risks or harmful applications.
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Table 5: Key Hyperparameters for Training Stages.
Component
Hyperparameter
Component
Hyperparameter
Learning Rate 2.0 x 10
Global Batch Size = 128
Gradient Accumulation
Micro Batch Rollout
Number of Epochs LR Scheduler = Cosine
Max Grad Norm 1.0
Learning Rate (Ir) = 1 10
Warmup Ratio 0.05
Weight Decay x 10-2
Cold Start
Max Sequence Length 32768
Actor
Entropy Coef Init (3)) = 0.02
Precision = BF16
Entropy Coef Min (Bmin) =
DeepSpeed Zero2
Entropy Decay Rate (A) 0.985 (exp)
Entropy Warmup Steps (T..) 140
Total Updates 200000
Max Prompt Length 8192
Adv Estimator = grpo KT. Penalty Type = low var kl KL Coef 10
Max Response Length = 8192
GRPO
Model Settings
Rollout Batch Size = 512
Generation Temperature = 1.0
T= = 0.3
Generation Top P = 0.95
APPENDIX
APPENDIX CONTENTS
A LLM Usage Statement
17
Training settings
17
Algorithm in Prioritized Advantage Distillation (PAD)
17
D Reasoning Example
18
A LLM USAGE STATEMENT
In this paper, we employed a Large Language Model (LLM) in an assistive capacity for this manuscript. Its use was strictly limited for grammatical accuracy and refining sentence structure to improve clarity and readability. All intellectual contributions, including the formulation of ideas, data analysis, and the composition of the manuscript, are entirely the work the authors.
B TRAINING SETTINGS
The training process can be divided into three distinct phases: cold start, multimodal reinforcement learning, and text-only reinforcement learning. Key hyperparameters for each training phase are detailed in Table 5.
C ALGORITHM IN PRIORITIZED ADVANTAGE DISTILLATION (PAD)
The PAD mechanism, introduced conceptually in the main text, is detailed in Algorithm to clarify its step-by-step operation in refining training batches for more effective learning.
Initially, PAD filters the original batch B create "effective set" & of sample indices and corresponding map Ag for their advantages (Lines 2-10 Algorithm 1). For each sequence in B, its absolute advantage [ computed. this value falls within specified informative range [Too Thigh), where Tlow > 0 is crucial for excluding stagnant (near-zero advantage) samples, the index i is added to E, and its absolute advantage A is stored in dg-
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Accuracy Comparison Across General Textual & Multimodal Benchmarks
ThinkLate-/BAA
MASF30
VL-eetmmkee77
Thinker 3B
34M Euroka Qwen 7B
ReVisusl
ReVicuall (Ours)
70
67.0
Qwen2. SVLL-B E-sssttuctt
60.9
57.0
Accuracy (%)
54.4
50
44.5
30
MMMU-PRO
MMMU
CMMMU
MMStar
MMLU-Pro
Avg.
Benchmarks
Figure 5: Performance comparison on general textual and multimodal benchmarks.
Table 6: Performance comparison on general textual and multimodal benchmarks. Best results per group (3B
7B) are bold, and second best per group (3B or 7B) are underlined.
Model
MMMU
MMMU-PRO
CMMMU
MMStar
MMLU-Pro
Avg.
Models
Qwen2.5-VT.3B 1L--VTT--B-Isttmuu
49.78
32.64
45,93
51.61
41.24
44.64
52.00
44.58
55.15
46.48
3B
44.92
44.33
Revisual-R1--
64.78
44.47
51.19
53.76
51.82
53.60
7B Models
Owen2.5-VL-7B-Insttn
52.00
42.52
$1.69
61.71
$1.80
51.94
ThinkLLit--B-VL
53.00
41.01
50.51
64.73
52.74
52.34
VI. Rethinker:7R
59.78
45.59
52.54
63.92
53.31
55.03
MMM-Eureka-Qwea-777
56.22
52.20
64.16
Revisual-R1-77
56.95
this effective set E is non-emply, prioritized sub-sampling is performed (Lines 12-29). This multi- step process involves: (a) Calculating sampling probabilities P, for each sequence index j E via temperature- controlled Softmax distribution over their stored absolute advantages Ag (Lines 14- 21). A uniform probability distribution across & serves fallback mechanism should the Softmax normalization term Z be zero. (b) Determining the sub-sample size k' as min([pN]] [&1). where p is the Lub-sampling ratio and N is the original batch size (Line 23). (c) Sampling k' indices from according to the calculated probabilities Paist (Line 25), (d) Constructing the final distilled mini- batch Basttilldd retrieving the original sequences corresponding these k' sampled indices (Lines 27-29). If E is void, an empty batch is returned. This entire procedure ensures that training batches enriched by systematically filtering out uninformative data and prioritizing samples anticipated to yield more substantial learning signals.
D REASONING EXAMPLE
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Algorithm 1 Prioritized Advantage Distillation (PAD)
Require: Original batch {seau..... segv) } with N [8]; advantage estimates Acst = (Ai...., AN) thresholds Tlow, Thigh temperature т; subsampling ratio p Ensure: Distilled minibatch Baistilled
Steps & 2: Per-Sequence Advantage Calculation and Effective Sample Filtering
D indices for effective samples Map: original index + its absolute advantage A,
2: Ag 1}
3: for it to do
Alabs + Asl
b Absolute advantage of current sequence i
5:
if Too Atabs I Thigh then
E + {i}
Add index to effective set Store absolute advantage effective sample
d Aquus
end if
9: end for
10: Baistilled 4- 0
11: if | then
Step Prioritized Sub-sampling from the Effective
b a. Calculate sampling probubilities P3 for each j E b Normalization term (Softmax denominator over E)
12:
Z Σ (x1((Ae61/T)
JEC
13:
Paist (} for all j E do
Map: original index j E its sampling probability P3
15:
if 7 0 then
16:
Paint -- exp(Aelil/r)/z
17:
else
18:
Patsii <- 1/|8|
D. Uniform fallback Z
19:
end
20:
end for
b. Determine actual sub- sub-somple size K!
21:
M min([pN], [El)
Sample k indices from & according to probabilities Pasa Ssampled Indices is list of k' indices from E Form the distilled mini mini-batch
22:
SsampledLindiees SAMPLE(E, Paist; K.)
23:
for all idæ E Seampled_indicee indices do
24: Baietilled &- Baietilled [seqidz]
25:
end for
26:
end if
27:
return Baistilled
19

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Published as a conference paper at ICLR 2026
Question:
Which of the boxes comes next in the sequence?
Select answers from A-E
Ours Revisual-RI 7B Model:
<think>
Okay, let's to figure out the sequence here. So there are five options lebeled A E; each with some black squares different positions. The problem is asking which comes next in sequence.
First, I need to look at the given figures and see if there's pattern. Let me list them out again make sure I have them right. Top row:
A:A:A square divided by diagonal, with black square the bottom right comer.
2. B: Two vertical stripes, each with black square on the right side.
3. C:A square with disgocal from tup left buttom right, and black square in the top left quadrant.
vertical stripes, with black square the right stripe's upper part.
5. E: square split into two vertical parts, with black square the left part.
per their order the problem. The now is options to 5, thon second row E. Walt, the problem shows the first now as the original five, then the next row labeled A to E.
|Reflection) Wait, may be should number
The first zow has live images (probubly labeled but the user then labels the second row as A to E. Wait, the actual question's options are A to E, each the second row. The sequence seems to involve the placement the black squares relative the dingonals or divissios of the square. I me analyze each
iRevisit Image) Wait, looking back:The
presented the problem with
option:
Original top row (before the line)......
Then the next
labeled to E: Square split vertically, left third black square.......
Sell-CritieJWaii perhaps the original problem's figures are arrangod follows:
Finst row (top):
Second (labeled to
Vertical division. left part has black square.......
ogie) New, trying to see the pattern. Lets look at the original figures (before the line) and then the options A-F. The task is to detennine which comes next Looking
first row's figures (original positions)......
[Self- Critic/Wal WWit, may be the pattern alternates between diagonal and vertical splits. Let's check:
Original sequence (top row):l. Diagonal, lower right...... the next (A-Ei If the pattern is altemating between diagonal and vertical splits, then after figure 5 (diigonal)), the next would he vertical split. potthessi Looking at original vertical splits: figures
Vertical split tthree parts?), part black.......
E. Let's soe: Original vertical splits (two purts) are figures 2 (lower right), 4 (upper right). Then after figure 5 (diagonal), next could be amother vertical split. Among options A-E, which are vertical spliss: A: vertical split into three columns? Or two columns? The image for A is a right, with black square in left part. vertical split into rwo, with black right upper.
then maybe be
square split verically into
vertical split into two, black in loft lower. after figure (diagonal),
next might vertical split, The last vertical split the original sequence (figure 4) the black square the upper right. Then the next vertical split could be the lower left (E) or upper left (A?), but need to cheek the order:
[H Hoothessis & Amalysis] Altematively, looking at the posilions maaioiinn Original vertical splitssiniFFigune 2: right lower Figure 4: right apper inlaão
pattern vertical splits altermating between lower and upper the right section, next
lower
right agasin? But figure 2 right lower, figure 4 is right upper. So maybe the next vertical split would be right lower again. Then option left lower. Hmm,
Alternatively, mayhe the sequence moving in certain direction. Let's think of the figures as arranged rows. The first row has five figures, and the second row has A-F. The correct answer is among A-F, which are the second row.
(Hy puthesis] Alternatively the sequence might be based on rolation or the position the black square. For example, slarting with diagonal, then vertical splits. Let's sec...... Wait, the options A-E are all vertical splits except B and C, which have diagenals. Walt, original figures 1-5 have mix: 1,3,5 have dingonals;; 2,4 have vertical splits. The options A-E: A vertical split left; diagonal with black in upper right, C is diagonal upper left; D is vertical split right upper; E is vertical split left lower.
after figure 5 (diagonal lower right), the next figure likely vertical split. Now, the original vertical splits (figures and figure 2 right lower, Figure 4 right upper. maybe the next vertical split alternates. If Figure 4 was right upper, (hen next could be right lower again (figure 2's position).
[Reflection] option left lower. Hmm. Altermativel, vertical splits the original sequence go from right apper, If the next vertical split right lower again, but that's figure 2. But F. is left lower.
right
[H' puthesis] Allemmstiveyy, may be the direction moving left.
Wait, maybe the pattern that after two diagonal figures (1.3.5), the vertical splits alternate between right and left. Let's sec'ninnriginal
vertical splits figure 2 (right lower), figure 4 (right upper). Then the next vertical split might he left lowee (E) or left upper (A). But figure A
squar, which vertical split left. l'e pallem moving from right lett, then after figure 4 (right upper), next would
seft lower (E).
CeitiCr But need check. Altematively, looking the pasitions of the black squares the vertical splins/nialiigu Figure 2: right third,
lower part. alaauur 4: right third, upper part.
So the vertical split's black square moves from lower to upper the same right third. Then, if the next vertical split is in the left third,
maybe lower part (E). So after moving right upper, next left lower. That would make the pattern: right lower, right upper, lower.
s. A Allematively. the answer is E.
Self- Critic] Let's check the options: Original sequennee: dizgonal, vertical split tright lower), diagonal, vertical split cright upper).
diagonal. Then next vertical split (lef lower, E). That socms Another way: looking at the positions of the black squaree in the
vertical splits. The vertical splits in figures and 4 have the black square the right third. Figure 24 black square at the lower part of the upper part.
right third, figure i's
So if we follow that pattern, the next vertical split would be in the left third, perhaps at the lower part (E). So the answer is En
</think>
<summary>>
sequence involves alternating between diagonal and vertical split figures, After the diagonal figure the next vertical split. The vertical splits the original sequence (figures and 4) have black squares the right third, moving from lower to upper. The next vertical ppli follows this putsen but the left third, at the lowar part.
Answer: (busedjE)
</summary>
Figure 6: Our Revisual-R1 model reasoning case, showcasing its exceptional reasoning ability. The model generates long responses, continuously hypothesizing, reflecting, verifying, and correcting to arrive at the final answer, while also providing a summary answer
20


Reviews:
[{"review_id": "pBnuwJWwTD", "reviewer": "Reviewer_EgX9", "summary": "This paper introduces ReVisual-R1, a 3B/7B multimodal large language model (MLLM) that achieves sota performance on challenging multimodal and text reasoning benchmarks. The key innovation is a three-stage curriculum: (1) a text-only cold-start with complex reasoning data, (2) multimodal RL with a novel Prioritized Advantage Distillation (PAD) to combat gradient stagnation, and (3) a text-only RL polish.", "questions": "- Have you tried the proposed curriculum on LLaVA-NeXT or InternVL models? If not, do you expect PAD to remain effective when the vision encoder differs significantly?\n- After text-only cold-start SFT and text-only RL, visual related ability could degrade. Did you measure vision-only tasks (e.g., OCR, object count) before & after text-only training to confirm no catastrophic forgetting?\n- How about the performance of sft training of mixture on multimodal and text data compared to the text-only setting?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- First to show that a text-only cold-start outperforms multimodal cold-starts for MLLM reasoning; PAD is a new fix for GRPO gradient stagnation.\n- Impressive performances on both multimodal and text reasoning benchmarks.", "weaknesses": "- All baselines are Qwen-VL derivatives; no comparison with other backbone families (e.g., LLaVA-NeXT, InternVL, Flamingo-style).\n- Interpretability of text-only data employed in the training process of MLLMs.", "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": "WzALVbU2F4", "reviewer": "Reviewer_fCCk", "summary": "This paper presents ReVisual-R1, an open-source multimodal large language model (MLLM), with the following key contributions:\nGRAMMAR Dataset Construction: The authors curate a diverse dataset covering textual and multimodal problems, with a focus on reasoning complexity and data diversity.\nStaged Reinforcement Optimization (SRO): A two-stage RL training framework is proposed, including Multimodal RL (MRL) and Textual RL (TRL), which strengthen cross-modal reasoning and pure textual reasoning capabilities, respectively.\nPrioritized Advantage Distillation (PAD): A novel method to prioritize informative samples during training, mitigating the gradient stagnation problem in GRPO and improving learning efficiency.\nExperimental results show that this approach achieves significant improvements over existing open-source models and is competitive with some commercial models, demonstrating the effectiveness of the proposed data strategy and training methodology.", "questions": "Training cost and efficiency: What are the computational overhead, training time, and memory requirements of SRO + PAD compared to standard GRPO? \n\nDataset ablation: How do different components of the GRAMMAR dataset (text vs. multimodal, difficulty levels)  contribute to the overall performance?\n\nHow would the model perform if trained or evaluated on other open-source multimodal reasoning datasets?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Comprehensive and well-structured: The paper provides a complete workflow from problem formulation, cold-start analysis, dataset construction, staged RL training, to ablation studies and generalization evaluation. Innovative approach: The combination of dataset curation (GRAMMAR) with staged RL training (SRO) and sample prioritization (PAD) represents a novel methodology in open-source MLLM research.\n\nEmpirical effectiveness: Extensive experiments across multimodal mathematics, logic, and textual reasoning benchmarks show clear performance gains at both 7B and 3B model scales.\n\nRationale behind staged training: The design of MRL followed by TRL is reasonable—first grounding visual-textual reasoning and then refining textual reasoning and linguistic fluency, allowing the model to generate longer, reflective CoT reasoning.", "weaknesses": "Training cost not analyzed: The computational overhead, training time, and data annotation cost of SRO + PAD are not quantified, which is important for assessing the method's practicality and scalability.\n\nDataset contribution ablation missing: The ablation studies focus on MRL/TRL phases and PAD, but the contribution of the GRAMMAR dataset itself (e.g., sample quantity, difficulty levels, multimodal vs. textual content ) is not independently analyzed.\nSelf-constructed dataset and generalizability: The use of a custom dataset, while common in multimodal reasoning research, may limit reproducibility and comparability. \n\nDiscussion on how this dataset design affects generalization would strengthen the work.\nPotential limitations in generalization: Although the model is evaluated on several knowledge-intensive benchmarks, its performance in completely different domains or real-world open-ended scenarios remains untested.", "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": "RoZMpjiyRP", "reviewer": "Reviewer_1SV1", "summary": "This paper proposes a three-stage training paradigm named ReVisual-R1 to enhance the performance of MLLMs on complex reasoning tasks. The paradigm consists of:\n\n1. **Text-centric Cold Start**: Initializing the model with high-difficulty, structured pure-text reasoning data to establish a strong foundation for language-based reasoning;\n2. **Multimodal RL**: Introducing a PAD mechanism to alleviate gradient stagnation caused by sparse rewards in standard GRPO under multimodal settings;\n3. **Text-only RL**: After multimodal training, applying pure-text RL as a \"polishing\" phase to further refine linguistic expression and logical consistency without compromising visual grounding capabilities.\n\nThe method significantly outperforms existing open-source 3B/7B models on multiple multimodal and text-only reasoning benchmarks, including MathVerse, AIME24/25, and MATH-500.", "questions": "1. **Is the complexity of the multimodal RL data insufficient?**  \n   The authors observe that an additional pure-text RL phase after multimodal RL is necessary to achieve optimal performance. Does this imply that the current multimodal RL dataset still lacks sufficient reasoning depth or diversity to support end-to-end training on its own? If a multimodal RL dataset with complexity comparable to the text-only RL data were constructed, could the subsequent text-only RL phase be eliminated?\n\n2. **How is the training balance between multimodal RL and text-only RL determined?**  \n   The paper does not specify the training balance between the two RL stages. Is the final performance sensitive to this balance? \n\n3. **Does the text-only RL phase degrade visual capabilities?**  \n   Although the authors claim that text-only RL “without eroding the newly acquired visual grounding”, the experimental evidence supporting this assertion appears limited.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. **High-quality data construction pipeline**: The authors introduce the **GRAMMAR** dataset, employing a well-designed pipeline involving rule-based filtering, difficulty-level grading, embedding-based clustering (NV-Embedding + HDBSCAN), topic annotation, and hierarchical sampling. This ensures both data diversity and high reasoning complexity.\n2. **Well-motivated training paradigm**: The three-stage curriculum learning approach (cold start → multimodal RL → text-only RL) is logically sound. Experiments validate the necessity and sequence sensitivity of each stage, particularly highlighting the unique value of **text-only RL as a polishing phase**.\n3. **Openness and reproducibility**: Detailed training configurations and hyperparameters are provided, and the effectiveness is demonstrated across both 3B and 7B model scales, offering strong value to the research community.", "weaknesses": "1. **Lack of multi-seed ablation studies**: The main experiments and ablation studies do not report variance across multiple random seeds, making it difficult to assess whether the observed performance gains are statistically significant—especially on high-variance benchmarks like AIME24/25.\n2. **Empirical rather than analytical justification for text-centric cold start**: While the authors support their claim with metrics such as response length and pass rates to demonstrate higher complexity in text data, they do not deeply investigate why multimodal cold-start data fails to effectively stimulate reasoning capabilities (e.g., due to vision-language alignment noise, annotation quality, or task design 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": "dzK3rOCbxV", "reviewer": "Reviewer_SyVL", "summary": "This paper presents ReVisual-R1, a novel post-training methodology aimed at enhancing the reasoning capabilities of MLLMs. This paper proposes a principled three-stage training curriculum, consisting of: (1) a text-only cold-start initialization, (2) a multimodal reinforcement learning phase, and (3) a text-only reinforcement learning refinement. The effectiveness of the proposed approach is evaluated on a range of multimodal and textual reasoning benchmark datasets.", "questions": "1. Please clarify the issues mentioned in the Weakness section, as they are not sufficiently explained in the current draft.\n\n2. There are a few minor issues:\n\n   - In Figure 1 (bottom),  ‘Traj A Tra B Tra C’ --> ‘Traj A Traj B Traj C’.\n\n   - Some arrow symbols in the figure appear to be partially obscured; please fix this.\n\n   - On line 459, merge the citation marks for consistency.\n\n   - The paper does not discuss limitations, failure cases, or future work, which makes it difficult for others to follow up. Please add a section addressing these points.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The methodology proposed in this paper is easy to understand, and the algorithmic descriptions are presented in a clear and accessible manner.\n2. Compared to other reasoning enhancement approaches for MLLMs, this paper also conducts comparative experiments on text-only benchmarks.", "weaknesses": "1. This paper exhibits some instances of over-claiming. Although the authors assert that their approach achieves SOTA results among 3B and 7B open-source MLLMs (lines 24-25), to my knowledge, several representative models, such as **MIMO-VL** and **Keye-VL* demonstrate significantly superior performance compared to the method proposed in this work. Additionally, in lines 42-43, regarding \"The direct application of text-centric RL,\" I believe the methodology presented does not fundamentally depart from existing RL algorithms, such as **GRPO** and **DAPO**. While achieving SOTA or radical innovation is not strictly necessary for a publication, the actual results and contributions in this paper deviate from the claims made by the authors, which raises concerns.\n2. The contributions of this paper are largely similar to those in prior works. For example, Contribution 1 has already been discussed in **LMM-R1**, and Contribution 2 can be found in papers such as **DAPO**. Thus, the novelty of this paper is relatively limited, and it leans more towards engineering practice rather than academic innovation.\n3. The paper should clarify whether its primary contribution lies in proposing a novel training paradigm or in the specific model trained using this methodology. These two aspects are fundamentally different. If the focus is on the training paradigm, then experiments limited to Qwen2.5-VL7B/3B are insufficient. It would be more compelling to demonstrate the effectiveness of this approach across a broader range of models, such as LLaVA and InternVL.\n\n\nTeam, Xiaomi Mimo, et al. \" Mimo-vl technical report.\"  arXiv preprint arXiv:2506.03569 (2025).\n\nTeam, Kwai Keye, et al. \"Kwai Keye-VL Technical Report.\" arXiv preprint arXiv:2507.01949 (2025). \n\nShao, Zhihong, et al. \"Deepseekmath: Pushing the limits of mathematical reasoning in open language models.\" arXiv preprint arXiv:2402.03300 (2024).\n\nYu, Qiying, et al. \"Dapo: An open-source llm reinforcement learning system at scale.\" arXiv preprint arXiv:2503.14476 (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)