SketchThinker-R1: Towards Efficient Sketch-Style Reasoning in Large Multimodal Models
Authors: Ruiyang Zhang
Dongzhan Zhou
Zhedong Zheng
Venue: ICLR 2026 Poster (2026)
Keywords: Efficient Reasoning
Large Multimodal Models
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Despite the empirical success of extensive, step-by-step reasoning in large multimodal models, long reasoning processes inevitably incur substantial computational overhead, i.e., in terms of higher token costs and increased response time, which undermines inference efficiency. In contrast, humans often employ sketch-style reasoning: a concise, goal-directed cognitive process that prioritizes salient
information and enables efficient problem-solving. Inspired by this cognitive efficiency, we propose SketchThinker-R1, which incentivizes sketch-style reasoning ability in large multimodal models. Our method consists of three primary stages. In the Sketch-Mode Cold Start stage, we convert standard long reasoning process into sketch-style reasoning and finetune base multimodal model, instilling initial sketch-style reasoning capability. Next, we train SketchJudge Reward Model, which explicitly evaluates thinking process of model and assigns higher scores to sketch-style reasoning. Finally, we conduct Sketch-Thinking Reinforcement Learning under supervision of SketchJudge to further generalize sketch-style reasoning ability. Experimental evaluation on four benchmarks reveals that our SketchThinker-R1 achieves over 64% reduction in reasoning token cost without compromising final answer accuracy. Qualitative analysis further shows that sketch-style reasoning focuses more on key cues during problem solving.

Published as a conference paper at ICLR 2026
SKETCHTHINKER-RE TOWARDS EFFICIENT SKETCH- STYLE REASONING IN LARGE MULTIMODAL MODELS
Ruiyang Zhang 1.2
Dongzhan Zhou Zhedong Zheng't
FST and ICI, University of Macau, China" Shanghai AI Laboratory, China
https://githin com Ruiyang- 061x/ Sketcarninker R1
ABSTRACT
Despite the empirical success of extensive, step-by-step reasoning in large mul- timodal models, long reasoning processes inevitably incur substantial computa- tional overhead, ie., in terms of higher token costs and increased response time, which undermines inference efficiency. In contrast, humans often employ sketch- style reasoning: concise, goal-directed cognitive process that prioritizes salient information and enables efficient problem-solving. Inspired by this cognitive ef- ficiency, we propose SketchTTinker-Rl, which incentivizes sketch-style reason- ing ability in large multimodal models. Our method consists of three primary stages. In the Skeich-Mode Cold Start stage, we convert standard long reasoning process into sketch-style reasoning and finetune base multimodal model, instill- ing initial sketch-style reasoning capability. Next, we train SketchJudge Reward Model, which explicitly evaluates thinking process of model and assigns higher scores to sketch-style reasoning. Finally, we conduct "ketch-Thinking Reinforce- ment Learning under supervision of SketchJudge to further generalize sketch- style reasoning ability. Experimental evaluation on four benchmarks reveals that our SketchThinker achieves over 64% reduction in reasoning token cost with- out compromising final answer accuracy. Qualitative analysis further shows that sketch-style reasoning focuses more on key cues during problem solving.
INTRODUCTION
Since the introduction of Large Reasoning Language Models (LRLMs), such OpenAl o1 (Jaech jet al. 2024) and DeepSeek-RI (Guo et al.] (0225) the deliberate, slow thinking ability has been extensively explored in language models (Zhang et 2025 Xu et al. 2025a Wang al. 2025d). Inspired by this rapid progress, similar reasoning abilities are being investigated in large mu multmodal models (Zhou et al., 2025a Li al. 2025b; Wang et al. 2025b). These models, through lengthy reasoning procedures, have demonstrated clear improvements across various visual recognition and
reasoning tasks (Huang et al. 2025 Yang et al. 2025 Shen et al. 2025a
However, such extensive reasoning often incurs high token costs (Han et al. 2024) and longer re- sponse times (Sui et al. 2025). This inefficiency limits their applicability
time scenarios and negatively affects user experience in interactive settings. Moreover, overthinking can harm correct- ness: redundant steps can introduce misleading information, which could compromise the reasoning efficacy (Chen et al.) 2024b) and small errors can accumulate over long chains of reasoning and finally lead to wrong answer (Cuadron et al. 2025). In addition, lengthy reasoning traces are often difficult for humans to interpret, obscuring the core logic behind predictions.
In contrast, sketching, a natural human behavior, offers both efficiency and effectiveness problem- solving (Cross) 2006p. By quickly writing down the essential steps and logic, humans reach accurate solutions. Notably, sketch-style reasoning focuses only on the critical steps, keeping the process concise while still leading to correct answers. Inspired by this cognitive behavior, we investigate whether similar reasoning pattern can be developed in large multimodal model to improve its rea- soning efficiency without sacrificing accuracy.
In this paper, we propose SketchThinker-RI. reinforcement learning framework which incentivizes sketch-style reasoning in large multimodal models to facilitate reasoning efficiency. Our approach
"Equal contribution. 'Correspondence thedonghhnn@umm.cu.m..

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Published as a conference paper at ICLR 2026
300
Vanilia-11 SketchThinker-At
Vanilla-RI
70
+1.8
Avg. Thinking Cost (Tokers)
250
SketchThinker-@0
Save 70.4%
66.5%
50
+1.9
200
Save 64.7%
Accurday
40-
+0.7
30
+0.2
100
10
Mathiissoo
VisuLogic
Phyx
VisuLogic
PhyX
(d)
(b)
Vanills-RR R1
M you war Ma AA yan
SketchiThinke--i R.
wn M MMM
Reward Acpurdny
0.5
Reaporse Length:
250
Vonilla-R
200
150
0.2
Step (c)
Stop
(d)
Figure 1: Our SketchThinker-RI significantly reduces the thinking cost without compromising fi- nal answer accuracy. Vanilla-RI serves as the baseline, representing the standard R RI-style trained model. Evaluation across four benchmarks from diverse domains shows that our model achieves comparable even superior performance (see (a)). At the same time, reduces thinking token cost by more than 64% (see (b)). During RL training, sketch-style reasoning consistently yields higher accuracy rewards (see (C)) while maintaining a much shorter reponse length (see (d)).
encourages models generate reasoning traces that preserve only the key logical flow required to solve problem, thereby reducing thinking cost while maintaining accuracy (see Fig. D The frame- work consists of three stages: (1) Sketch-Mode Cold Start. We construct sketch-style reasoning data and perform supervised fine-tuning on base multimodal models to instill initial sketch reason- ing ability. Specifically. we leverage existing multimodal reasoning datasets that contain detailed, long-form reasoning. Utilizing strong proprietary LLM, we convert thinking style of these longform reasoning into concise sketch-style, preserving essential logic and removing redundant details. (2) JuddchJudge Reward Model. In this stage, we utilize the two-mode thinking data (sketch-style and normal reasoning) from cold start stage to further train an LLM to become SketchJudge re- ward model. This SketchJudge reward model explicitly evaluates reasoning traces, assigning higher scores to concise, sketch-style reasoning while penalizing unnecessarily verbose explanations. provides reliable supervisory signal for the following reinforcement learning process. (3) Sketch- Thinking Reinforcement Learning. Finally, we perform reinforcement learning on the cold-started model under the guidance of SketchJudge to further generalize the sketch-thinking ability. Our ward design explicitly incorporates evaluation from SketchJudge, encouraging models to produce sketch-style, concise reasoning traces. The reinforcement learning is conducted on datasets across diverse domains to ensure the generality and robustness of the learned sketch-thin sketch-thinking capability. We evaluate SketchThinker--K R/ across four benchmarks spanning different domains. Our model achieves over 64% reduction in thinking token cost, while maintaining and even improving final answer accuracy. Qualitative analysis further reveals that sketch-style reasoning focuses on the key logical steps necessary for problem-solving, offering both efficiency and interpretability. Our main contributions are summarized as follows:
Sketch-based Reasoning Framework. We introduce SketchThinker-RR RI, novel reinforcement learning framework that fine-tunes Large Multimodal Models (LMMs) to produce concise, sketch- like reasoning chains. By directly rewarding brevity and correctness, our method guides the model distill complex reasoning into its most essential steps.
State-of-the-Art Reasoning Efficiency. Extensive experiments on four multimodal reasoning benchmarks validate that SketchThinker-Rl reduces the token cost of the reasoning process by over 64% with no degradation in final answer accuracy, setting a new benchmark for efficient multimodal reasoning.
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Cold Start
(II) Reward Model
(III)
Q. Vlong
Trong
Base Model
Reference Model
LLaVA-COT
Milafor MathViiiin LogicVista SeePhys
Group
Ao
Vislon-LLoood
Skelchhidge
Policy Model
Computation
Q. Trunsch
Tzbwtzhh
Yo
Skelchihugge
TO
Ao
SFT Model
Advantage
Figure 2: Overview of our SketchThinkeras pipeline. (1) In the Sketch-Mode Cold Start stage, we convert long reasoning processes from existing multimodal reasoning datasets into sketch-style, and fine-tune the base multimodal model to instill initial sketch-style reasoning ability. (2) Next, we train a SketchJudge Reward Model, which favors sketch-style reasoning and penalizes overly verbose reasoning. (3) Finally, perform Sketch-Thinking Reinforcement Learning the cold- started multimodal model under the supervision of the trained SketchJudge reward model, further enhancing the sketch-thin serechhtiinkinggge ability.
METHOD
2.1 SKETCH-MODE COLD START
The first stage of our SketchThinker44 framework is the sketch-mode cold start. This stage aims instill initial sketch-style thinking ability into base multimodal model, laying solid foundation for the subsequent reinforcement learning generalization process. Specifically, our data source is based on two multimodal reasoning datasets, e.g., LLaVA-CoT-IONe (Xu et al. 2024) and Vision- R1-cold (Huang et al. 2025). These datasets already contain images I, questions Q. long chain-of- thought reasoning Long. and answers A. Based on such multimodal reasoning data, we convert the long, detailed reasoning process Toon into sketch-style reasoning process T Sketch-- We leverage strong LLM to conduct this transformation. (1) The first aspect is minimizing the content while keeping the key logical flow. Irrelevant details and verbose explanations specific examples. (2) The second aspect converting the reasoning into numbered list format, with key steps separated and clearly listed. The full prompt for the sketch-style reasoning data generation is provided in Appendix A The supervised fine-tuning process minimizes the following objective across all training samples:
removed, along with
1 N
LSFT
log no (oi. (1,et,
where is the number of training samples, 0i, denotes the f-th token of the output sequence for the i-th sample. T; is the total length of the output sequence the i-th training sample, oi,<t represents the tokens preceding the t-th token of the i-th training sample, is the input query for the i-th training sample, and Ty the model policy parameterized by 0.
Why not directly apply reinforcement learning to incentivize sketch-style think- ing? We find that letting base multimodal model directly explore sketch-style reasoning leads to quite slow learning process. As result, the eventual reduction in reasoning cost is only marginal. In contrast, performing sketch-mode cold start on the base model explicitly instills initial sketch- style reasoning ability, enabling more effective learning and exploration during the reinforcement learning process. The aketch-mode cold start stage substantially contributes to improvement of rea- soning efficiency. What are the characteristics of sketch-style reasoning? Sketch-style reasoning substantially shorter than long-form reasoning. It preserves only the essential logical flow in an abstract form. Consequently, cold-start training with sketch-style reasoning data foster highly efficient reasoning ability. At the same time, sketch-style reasoning retains all the key cues required to solve the problem, thereby ensuring strong problem-solving capability.
Discussions.
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2.2 SKETCHJCDGE REWARD MODEL
The second stage of our framework involves building the SketchJudge reward model, which ex- plicitly evaluates reasoning style of the model. It favors sketch-style reasoning and penalizes long. detailed reasoning. This reward model is then used to supervise the subsequent reinforcement train- ing process. guiding the model to generalize sketch-style thinking. In practice, we fine-tune an open-source L.L for this purpose. Specifically, we leverage both normal reasoning data Toong and sketch-style reasoning data Skketc from the cold-start stage to construct the fine-tuning dataset. Normal reasoning is assigned a score of O, while sketch-style reasoning is assigned score of 1. The template for building the SketchJudge fine-tuning data is as follows: "Give a score for sketch-style thinking and score of 0 for normal thinking. Normal thinking contains detailed anal- ysis. Sketch-style thinking contains only the key logic flow. Only output the final score. Now, score this thinking process: [THINKING]'. We utilize the same prompt for SketchJudge to evaluate the thinking process during the subsequent reinforcement learning stage.
Discussions. Why conduct supervised fine-tuning the LLM to build SketchJudge? Perform- ing supervised fine-tuning on the base LLM for reasoning-stie style evaluation can improve evaluation accuracy. Precise thinking-style reward signals make reinforcement learning more effective, leading to both reduced reasoning length and improved final answer accuracy. In contrast, directly prompt- ing an off-the-shee shelf LLM to evaluate reasoning style yields less reliable reward signals. Such noisy rewards can ultimately compromise the effectiveness of the reinforcement learning process. Why explicitly supervise the style of the reasoning? Our SketchJudge reward model only poses super- vision on the style of the reasoning process, with no strict restriction on the thinking length. This design can lead to more adaptive thinking behavior. Our SketchThinker-RI can still generate rel- atively long and extensive reasoning for challenging questions. Meanwhile, because the thinking style is strictly controlled, the average thinking cost is still significantly reduced.
2.3 SKETCH-THINGG THINKING REINFORCEMEN LEARNING
The final stage our SketchThinker-R framework is sketch-thinking reinforcement learning. This process is guided by the trained SketchJudge reward model and aims to generalize the sketch-style thinking ability established during the sketch-mode cold start stage. practice, we adopt the off- the-shelf Group Reward Proximal Optimization (GRPO) algorithm (Shao et al. (024).. Specifically, GRPO performs multiple rollout samplings and optimizes the policy to favor responses with higher assigned rewards, the training objective of GRPO is follows:
(0) P())(()) (O1g)
G
(a (q) As. clip Te (o: 9))
(o: 9)
min
1 1 1 A, - DKI(TO ll Tre)
Te (o: 4)
am1
(2)
<o 9) log (o;
Teet 0; To (o(o;
(9)
DKI
1,
(3)
where To is the current model policy, mew is the old policy, is the rollout group size, q is the query, is the i-th sampled reponse, Tet is the reference model, is the clipping hyper-parameter controlling updating degree, and B is the coefficient of Ballbacck-eibbbee (KL) penalty.
A r: mean (21, .rr))) std (d((fr1 ....,,,.. ,..".. rra))
(4)
A; is the normalized advantages computed based on rewards fri, r2, rr).
Our thinking style reward shaping is follows:
R = 0.5 X Reccuracy (o.) + 0.4 x Rforman (o,) ) 0.1 x Rninkinng-sytl(o..)..
(5)
where Raccuracy (o,) evaluate answer accuracy, Rtomaa (o:) check whether response match the required response format, Rahinking (O:) is the thinking-sty'e reward score assigned by our SketchJudge.
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The reasoning process is parsed from the final response of the model and fed into the SketchJudge to obtain this Sänking-style reward:
1
if SketchJudge checks the thinking process as sketch-style,
Rihinking style (0:)
(6)
0
if SketchJudge checks the thinking process normal-style.
3 EXPERIMENT
Dataset. SketttC We construct our cold start dataset based on LLaVA-CoT. 100K (Xu et al. 2024) and Vision-RI-cola (Huang al., 2025), both of which multimodal reasoning datasets that already contain long reasoning processes. We randomly sample 10K ques- tions from LLaVA-CoT-100K, and another 10K questions from Vision-RI-co Icolld resulting in a total of 20K questions. The long reasoning processes are extracted from the full model responses and then converted into sketch-style reasoning utilizing GPT-5. The detailed conversion prompt is pro- vided in the Appendix AA] SketchRL- To construct the RL training set, we draw from four data sources across different domains to enhance the generalization of sketch-style reasoning ability. MMStar (Chen et al.] 2024a) contains 1,500 multiple-choice questions for general visual under- standing tasks. Math Vista (Lu et al. 2023) focuses on mathematical visual reasoning, including both multiple-choice and free-form questions, with 5,140 samples in total. LogicVistt (Xiao et al., 2024) includes 448 samples targeting various visual logic skills. ScePhys (Xiang et al., 2025) con- tains 2,000 frre-form samples of visual physics questions. From each source, we randomly sample 250 questions, forming a final RL training set of 1,000 samples.
Implementation Details. For RL training, utilize Easy-RI (Zheng et al. 2025 We employ GRPO (Shao et al., (024) for training. The maximum prompt length is set to 2048 and maximum response length is 2048. The KIL coefficient is 0.01. We utilize Adam
optimizer, with learning rate of le-6 and weight decay of 1e-2. For rollout, we set sampling time to and sampling temperature to 1.0. Rollout batch size is set to 128. We train for 15 epochs, resulting in 105 training steps. For supervised fine-tuning during Sketch-Mode Cold Start and SketchJudge reward model training, we leverage LLaMA-Factors (Zheng et al. 2024). We use H200 for all our experiments.
Evaluation. We conduct evaluations on four multimodal benchmarks from various domains to en- sure a comprehensive assessment of our SketchThinker-RR R1. MMMU (Yue et al., (024) a compre- hensive benchmark designed to evaluate the general reasoning ability of large multimodal models, covering questions from wide range of topics and includes 100, 900, and 10,500 questions in the dev, validation, and test sets. Math Vision (Wang et al.) 2024) specifically benchmarks mathematical visual question solving ability, containing both free-form and multiple-choice questions, with 3,040 questions in test set and testmini set of 304 questions. VisuLogic (Xu et al. 2025c) primarily focuses on benchmarking logical reasoning ability, with particular emphasis on understanding visual infor- mation, which contains 1,000 samples. PhyX (Shen et al., (2025) is a recently proposed benchmark aimed at evaluating capability of model in visual physics question understanding, which consists of 3,000 visually grounded physics questions.
Baselines. Vanilla-R1: We perform vanilla RI R1-style training (Guo et al., 2025) on the base mul- timodal model with the same training data as SketchThinker-RI to establish this baseline. Con- strained CoT (Nayab et al., 2024): Directly prompt Vanilla-RI to restrict the reasoning process within specified word count. Chain-of-Draff (Xu et al. each reasoning step to certain word count. C30T (Kang et al. 2025): Mix both short CoT and long CoT data finetune the base multimodal model. VeriThinker (Chen et al. (025): Construct Short-CoT data using a small non-reasoning model, then finetune the base multimodal model on this data for verification task to build efficient reasoning ability. I.1 (Aggarwal & Welleck 2025): Conduct reinforcement learning with a sength-based reward to control the reasoning length, where the response length is compared with a fixed golden length, and the LI difference used as the length reward. ThinkPrune (Hou et al. 2025); First truncate the model response to a fixed target length, then calculate the accuracy reward based only on the truncated reponse.
2025b); Prompt Vanilla-RI constrain
Metrics. We adopt three metrics for evaluation. Acc. denotes answer accuracy. #Token represents the average token count of model reasoning process. In addition, we define a new metric, Efficiency of Thinking (EoTT), to explicitly measure the efficiency of reasoning. FoT is calculated as Ace Nekeen
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MMMU
MathVision
VisuLogic
PhyX
Method
Acc.t
#Token. B
Acc.f #Token],
BOTT
Acc.t
#Token.,
BoTT Acc.f #Token,
BoTT
Direct Inference
Vanilla-RI
61.0
182.2
0.335
31.0
221.1
0.140
27.6
240.0
0.115
46.7
225.1
0.207
Prompt-basee
Constrained CoT Chain-of-DD -rafff
58.6
78.2
0.749
26.2
79.2
0.331
26.4
71.5
0.369
42.4
73.4
0.578
58.9
86.3
0.683
27.4
85.4
0.321
26.5
94.6
0.280
42.2
85.2
0.495
Supervised-fire
fante-iinetnnn-bbaaee based
C
59.3
127.1
0.467
28.8
125.5
0.229
27.1
134.6
0.201
43.8
117.5
0.373
VeriThinker
60.1
105.8
0.568
29.1
152.5
0.191
27.5
107.4
0.256
45.5
132.7
0.343
Reinforcement Lenarrrnemmn---ariinnn Leased based
LI
59.5
136.8
0.435
29.5
146.7
0.201
27.2
167.4
0.162
45.1
153.4
0.294
ThinkPrune
59.2
104.9
0.564
29.6
136.3
0.217
26.9
183.2
0.147
46.3
163.7
0.283
SketchTbinker-RI... R1-7B
62.8
64.3
0.977
31.7
65.5
0.484
27.8
56.3
0.494
48.6
75.3
0.645
Table 1: Comparison with state-of-the-art efficient thinking methods. Quantitative results across four benchmarks from different domains validate the thinking efficiency of our SketchThinker-RI 7B. Compared with various baselines, including prompt-basets supervised fine-tuning, and rein- forcement learning methods, our model achieves higher accuracy with fewer reasoning tokens. We utilize Qwen2.5-VL. VL--VV-L--ttnte as backbone for both method and all baselines for fair compar- ison. Vanilla-RI refers to results of standard RI-style trained model.
MMMU
MathVision
VisuLogic #Token.,
PhyX #Token.
Method
Acc.1
#Token.
EoTT
Acc.f #Token]
EoTy
Acc.t
BoTy Acc.1
EoT1
Direct Inference
Vanilla-R1
54.8
128.3
0.427
26.9
151.2
0.178
25.6
139.5
0.184
34.8
173.2
0.201
Prompt-based
Constrained CoT
52.7
76.2
0.692
22.1
63.4
0.349
25.2
69.1
0.365
31.5
63.6
0.495
Chain-of-Drin Draft
53.8
72.1
0.746
22.7
71.5
0.317
25.1
74.2
0.338
32.5
71.2
0.456
Supervised-fing fin-iiedd-fe--tnnnnn -assdd
C3oT
54.1
107.5
0,503
24.1
105.1
0.229
25.1
104.3
0.241
33.1
93.2
0.355
VeriThinker
52.2
95.8
0.545
23.1
127.6
0.181
25.2
93.7
0.269
32.6
92.1
0.354
Reinforcement-I eeaereemmeet--erenngeee haxed
LI
53.7
102.6
0,523
24.7
107.0
0.231
25.2
97.6
0.258
32.2
83.4
0.386
ThinkPrune
53.2
95.2
0.559
23.8
92.3
0.258
25.4
73.3
0.347
33.2
82.2
0.404
SactchTThinkers RI-3B
55.9
54.5
1.026
25.3
72.7
0.348
25.8
36.9
0.699
35.1
67.3
Quantitative comparison between ShettchThinker--R1-BBB and other baselines. Our 3B model consistently outperforms various baselines in both answer accuracy and reasoning token cost. This validates the robustness of our proposed framework in eliciting efficient reasoning across models of different scales. All methods utilize Owwen.55-VL-3B--Instit
Table 2:
backbone.
3.1 MAIN RESULTS
We present quantitative comparison between SketchThinker-RI. and various efficient thinking baselines (see Tab. D We observe that our SketchThinkee-Rl--B surpasses various methods by clear margin in both final answer accuracy and thinking token cost. This validates the effective- ness of our proposed sketch-style thinking training pipeline, including sketch-mode cold start and sketch-thinking reinforcement learning. For prompt-based efficient thinking methods, the thinking process is forcibly constrained, leading to insufficient reasoning and significantly compromising fi- nal answer accuracy. For SFT-based efficient thinking methods, the ability is primarily fit to the training dataset, resulting in sub-optimal performance when generalizing to out-of-domain bench- marks. contrast, our SketchThinker R1 training framework builds inherent sketch- sketch-thinking ability into model, enabling it solve questions with concise and effective reasoning. Our model achieves

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Published as a conference paper at ICLR 2026
(a)
(c)
Sketch-Mode Cold Start Sketeb-Thinking Acort
#Token]
Bol'i
Cold Start Source
|Acc.
#Token]
BoT1
61.4
114.5
0.536
LLVAA-CoT-10OK
61.9
68.5
0.904
<
62.1
152.2
0.408
Vision-RI
61.2
71.2
0.860
62.8
LLaVA-CoT-100K & Vision-R -cold
62.8
64.3
0.977
(b)
Model
Closed Source
Source
Acc.t
#Token].
FoTt
SketchJudee Reward Model
SFT
Acc.t
#Token]
EaT1
GPT-5
62.8
64.3
0.977
Qwen2 5-7B Instruct
62.8
64.3
0.977
mini
62.2
0.953
Qwen2.5 5. Instruuttt
61.0
72.1
0.846
GPT-OSS-20B
60.5
63.2
0.957
Qwen2 5.3B Instruct
59.8
74.8
0.799
Qwen2.5-721B Instract|
59.1
62.3
0.949
Qwea2 5-3--nrstttttttttt
59.4
78.1
0.761
Table 3: (a) Ablation study of our primary stages. We observe that combining both sketch-mode cold start and sketch-thinking RL achieves the highest thinking efficiency. Applying only sketch-mode cold start mainly instills sketch-style reasoning ability fitted to the training data, resulting in limited performance on test benchmarks. In contrast, applying sketch-thinking RL without cold start leads to ineffective exploration and learning, yielding only marginal reductions in thinking cost. (b) Ablation study of the LLM used for converting long reasoning into sketch-style reasoning. We observe that cold-start data generated by GPT-5 yields optimal thinking efficiency. Therefore, we adopt GPT- Data from open- L.LMs reduce token cost
generated data to build SketchColdStart-200 more aggressively, but at the expense of lower answer accuracy. (c) Ablation study of data sources for constructing cold-start data. Combining diverse data sources simultaneously improves answer accuracy and reduces reasoning cost. Leveraging multiple sources enhances robustness and fosters more general sketch-style reasoning ability during the cold-start stage. (d) Ablation study of the SketchJudge reward model. Qwen2.5-777 5-B Instructtt,, after fine-tune fine-tuning for scoring ability, achieves the best thinking efficiency. Providing more accurate supervision signals during reinforcement learning leads more stable and effective training process, therehy enhancing the final sketch-style rea- soning ability. Best results are bolded. Gray line is our default setting. All ablation results are from MMMU. The utilized backbone is Qwen2.5-VL-7B-Instte
optimal thinking efficiency across various benchamrks. Compared with normally trained RI model, our model attains comparable answer accuracy with over 64% savings in thinking cost.
We also present the quantitative comparison results of our 3B model (see Tab, 2}. For the 3B scale, our framework also effectively elicits highly efficient reasoning ability. Compared with various baselines. including prompt-based, SFT-based, and reinforcement learning- based methods, our ap- proach achieves higher answer accuracy while requiring fewer reasoning tokens. This demonstrates the general effectiveness of our method in improving reasoning efficiency across different model scales. Notably, compared with Vanilla-R1, our method reduces thinking cost by more than 50% while maintaining comparable answer accuracy. Compared with the 7B model, the reduction rate of thinking cost is slightly lower. This is because the 3B model generally produces shorter reasoning chains with less redundancy, leaving lesser room for efficiency improvement.
3.2 ABLATION STUDY AND ANALYSIS
Ablation of primary stages. We present an ablation study of our primary stages, including sketch- mode cold start and keicch- reinforcement learning (see Tab. Ba). We observe that com- bining sketch-mode cold start with sketch-thinking reinforcement learning achieves both the high- est reasoning efficiency and the best answer accuracy. Relying solely on sketch-mode cold start build sketch-thinking ability that is largely restricted to the training set, resulting in suboptimal per- formance on test benchmarks. Conversely, applying -ketch-thinking reinforcement learning with- out initializing sketch-styke ability from cold start leads to ineffective exploration and yields only marginal reductions in thinking cost.
Ablation of the LLM used for Sketch construction. We report ablation results of different LLMs utilized to extract sketch-style reasoning from long reasoning traces during construc- tion of SketchColdStart- 20K (see Tab. 3b). We find that cold-start data generated by GPT-5 leads
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A-
F
B
B
D
Q: Three circies with centres A, B, C touch each other pairs from the outside. Their radil are 3,2 and How big is the of the triangle ABC7
Q: In triangle ABC, ABxAC, angle A=80". D, E, and F lle on BC, AC and AB, respectively, and CE=CD and BF=BD, then angle EDF equals 30 40 50 65
Vanilla-R1: To find the area of triangle ABC, we need to determine lengths of the sides of the triangle, which are the distances between the centers of the circles. The distance between the centers of two touching circles is sum their radii. 1. distance between A and
Vaniila-R1: <thinko Given that AB AC and angle A 80", triangle is isosceles with angle angle C= 50 Points D, E, and are such that CE CD and BF BD. This implies that triangle CDE and triangle BDF are isosceles triangles. Since
CD, angle CDE angle CED. Similarly, since BF BD, angle BDF angle BFD, We need to find angle EDF. First, let's find angle ECD and angle BDF. In triangle CDE, angle ECD = 180" Zangle CDE. Since angle CDE angle CED, \fracf180* aangle CH2) = \frac[180' 50"H2) 65". Therefore, angle ECD 180" 2 Atimes 65 50". Similarly, triangle BDF, angle BDF angle \frac[180' angle BH2] \fracf180" 50"H2) 65".
5. The distance between A and is 3 1 = 4. 3. The distance between B and C is + 3.Now, we have a triangle with sides AB 5, AC and 3. This
right triangle because 3'2
angle CDE
4"2 = 5"2 (l.e., 9 16 25). The area of a right triangle is given by: Area \frac(1H2) Atimes (base) Atimes (height). Here, the base and height are and So, the \frac[1H2) Atimes 12 6. Therefore, the area of
Area \frac(1H2) \times Vtimes
Now, angle EDF = 180' angle EDC angle BDF 180" .55 65 50" </thinko conswersC</answen
triangle ABC is 6.c/thinoo-nsswerrcc answer>
Thinking Token:
Thinking Token: 315
SketchThinker-RR cthinlo11. External tangency implies distances 2. AB 3+2, BC 2+1, AC 3+1 3. sides: 5, 4 3244=552 - right triangle 5. area = (aaea///aaaaeeee///////ooooa/eee
SketchThinker-RI <think>l. AB=AC, angle A=80", angle Brongle C=50". 3. CE=CD, angle CEDeangle CDE. 4, angle CEDeangle CDE=65 5. BF=BD, angle BFD=angle BDF. 6. angle BFD=angle BDF=65" angle EDF=180"- "5--665"00 "000".////// t/thnnkkanrweerr/eeeee /nsswerr
answer>
Thinking Token: 79
# Thinking Token: 112
Qualitative analysis of our SketchThinker-RL SketchThinker-Rl conducts highly efficient yet effective sketch-style reasoning process. By focusing on key cues in problem-solving our model arrives at the correct answer. The samples are from MathVision (Wang al. 2024p.
Figure 3:
to the most effective development of sketch-style thinking ability. Models cold-started with data from closed-source LLMs achieve relatively higher final answer accuracy, because closed-source models tend to produce more reliable sketch-style reasoning processes, which in turn improves ac- euracy after reinforcement training. In contrast, cold-start data generated by open-source LLMs results in more efficient reasoning ability, as these models tend to produce more concise sketch-style reasoning. Overall, distribution of cold-start data significantly inffuences final learned ability after reinforcement learning, shaping whether the trained model emphasizes accuracy or efficiency.
Ablation of the cold start data source. We present the influence of data source used construct SketchColdStart-205 (see Tab. Tcb. We observe that combining multiple data sources simultane- ously improves both final answer accuracy and reasoning efficiency. more diverse set of cold-start data enables the model to acquire broader and more general sketch-thinking ability without over- fitting to a single distribution. During the subsequent reinforcement learning stage, this broader capability can be more effectively generalized, resulting in further gains in reasoning efficiency.
Ablation of SketchJudge reward model. We present results of different SketchJudge reward mod- els (see Tab. 3d). We observe that D0wen.55-7B-Instrue after scoring ability fine-tuning, enables SketchThinker-RI to achieve optimal thinking efficiency. Providing a more reliable reward signal during reinforcement learning enables the model to more effectively learn sketch-style reasoning ability. Supervised fine-tuning enhances the scoring accuracy of the SketchJudge reward model, thereby improving reward assignment and guiding the learning of more effective sketch-style rea- soning. In addition, employing a larger backbone improves the evaluation of the model reasoning process during reinforcement learning, leading to stronger overall sketch-thinking ability.
Qualitative cases. We also present qualitative examples of our SketchThinker R1 (see Fig. 3 We observe that SketchThinker-PP generates reasoning processes that concentrate the key cues
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required to solve the problem, leading to highly efficient thinking process. Compared with Vanilla- RI, thinking cost of SketchThinker-B R1 is significantly lower, while still producing correct answers.
4 RELATED WORK
Large Multi-modal Reasoning Model. Driven by the success of RI. in eliciting reasoning from LLMs (Guo et al. 2025 Jacch al. (024), researchers have attempted replicate this success in the multimodal domain Li et al. [025b] Wang et al. 2025b). TisualThinker-RI-ZZ
(Zhou et al.) 2025b) and MM-Eureka (Meng et al., (022)) pioneer the exploration of spatial reasoning and mathe- matical VQA tasks, successfully reproducing long CoT characteristics in large multimodal models. Taking one step further, OThink -MR1 (Liu al., 2025) propose dynamic weighting of the KL di- vergence term to enhance GRPO (Shao et al. 2024)) balancing exploration in early RL stages with exploitation in later stages. ThinkLite-V VL (Wang et al.] 2025a) introduce difficulty quantification for training samples, measuring the iterations required to solve each problem and selecting samples of appropriate difficulty for RL training. Building on these carly efforts, Vision-R1 (Huang et al.) 2025) leverage VLMS and DeepSeek-RI (Guo et al., 2025 tt construct reasoning data, initialize rea- soning ability through cold-start training, and propose Pprogressive Thinking Suppression Training (PTST) to incrementally increase reasoning length. LMM-RI (Peng al., 2025) adopt two-stage pipeline, first leveraging large-scale text-only data to strengthen reasoning before transitioning to multimodal reasoning. Beyond text-image reasoning, some works extend other modalities (Xie) et al. 2025; Zhang et al. 22024a b). R1-Omni (Zhao et al. 2025) conduct preliminary attempts on video and audio tasks, while Spatial-RI (Ouyang (025) extend RI-style training video spatial reasoning. However, these models generally exhibit long reasoning processes, resulting in low ef- ficiency. In contrast, SketchTThinker-I explicitly optimizes the reasoning process while preserving answer accuracy by building sketch-style reasoning ability in large multimodal models.
Efficient Reasoning. Research on efficient reasoning has mainly followed three trajectories (Feng et al. 2025 Sui et al. 2025). The first is prompt-bused efficient reasoning, which controls reasoning length through instructions. For example, TALE-EP (Han et al., 2024) prompts models to estimate the required token budget for given question, then constrains reasoning within that budget. Chain- of-Draft (Xu et al. 2025b) further controls the length of cach reasoning step through prompt design. The second trajectory is training- -based efficient reasoning, which directly trains models to produce concise reasoning. These methods typically involve collecting short reasoning data and conduct- ing SFT. For instance, Self-Training (Munkhbat et al., (025)) samples multiple outputs for the same question, selecting the shortest reasoning to construct a training set. TokenSkip (Xia et al., (2225) estimates the semantic importance of each reasoning segment and prunes unnecessary tokens. The third trajectory is output- basput-based efficient reasoning, which reduces reasoning length dynamically at inference. Several works replace explicit CoT with latent representations. Coconut (Hao et al.) 2024) treats the final hidden states of an I.M as continuous thoughts, hypassing tokenized reason- ing. CODI (Shen et al.] (025c) extends this by training models to learn continuous latent CoT via self-distillation. Other works explore sampling- based methods to improve efficiency without retrain- ing (Li al. 2025a: Wang et al. 2025c). Different from existing works, SketchThinker RI explicitly supervises the thinking style of model during reinforcement learning through the SketchJudge ward model, thereby enhancing reasoning efficiency by fostering sketch-style thinking ability.
5 CONCLUSION
In this paper, we propose SketchTTinker-RI. reinforcement learning framework designed to incen- tivize efficient sketch-style reasoning large multimodal models. Our framework consists of three main stages: (1) Sketch-Mode Cold Start. We construct MoetchholdStart2201
sketch-style reason- ing dataset created by converting long reasoning processes from diverse data sources to sketch-style. By fine-tuning on this dataset, base multimodal models are endowed with initial sketch-style reason- ing ability. (2) SketchJudge Reward Model. We develop a reward model that explicitly evaluates the reasoning process and assigns higher scores to concise sketch-style thinking.
(3) Sketch-Thii Thinking Reinforcement Learning. We apply reinforcement learning under the supervision of SketchJudge to further generalize and strengthen the sketch-style reasoning ability. Extensive evaluations benchmarks across different domains illustrate the thinking efficiency of Sketch Thinker-RA. R11 which achieves over 64% reduction in reasoning length while maintaining comparable answer accuracy.
four
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ACKNOWLEDGMENT
We acknowledge supports from Guangdong Basic and Applied Basic Research Foundation 2025A1515012281 Nanjing Municipal Science and Technology Bureau 202401035 and University of Macau MYRGG.GRGG002-0007--5STUUDD
ETHICS STATEMENT
In this work, we investigate incentivizing sketch-style reasoning ability in large multimodal models to improve reasoning efficiency. The training data used in both the cold-start and reinforcement learning stages are drawn entirely from publicly available sources and contain no harmful or sen- sitive content. Our research focuses exclusively on improving the reasoning efficiency of large multimodal models, without involving human subjects, personal data, or safety-critical applications.
REPRODUCIBILITY STATEMENT
We provide detailed presentation of implementation process in Sec. [ and Appendix D The construction procedures of both SketchColdStart 20K and SketchRL- IK are described in detail. All key hyperparameters used in supervised fine-tuning and reinforcement training are reported. In addition, we include the full prompts employed for sketch-thinking data construction. All code, models, and datasets will be released to ensure reproducibility.
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APPENDIX
A PROMPT DESIGN
We present the prompt used for sketch-style reasoning generation (see Tab. T6.. The LLM is in- structed retain the key logic for problem solving while removing unnecessary details. In addition, it is prompted to structure the reasoning process into a numbered list, making the reasoning clearer and easier follow.
B COLD START DATA ILLUSTRATION
We provide several examples from cold-start dataset SketchColdStar-20K (see Fig. 4 The generated sketch-style reasoning process is highly concise. Fine-tuning the base multimodal model on SketchColdStart-200 establishes solid foundation for developing efficient reasoning ability.
C QUALITATIVE CASES
We present qualitative cases of SketchThinker-RI (see Fig. Fig. 6 Fig. Fig. 8 SketchThinker- RI generates substantially shorter reasoning processes while still arriving at correct answers. Its sketch-style reasoning emphasizes key cues for solving the given questions, resulting in much more efficient yet highly effective reasoning process. Moreover, the sketch-style reasoning offers greater explainabilityy making casier to understand and follow the key logical flow.
D IMPLEMENTATION DETAIL
D.1 SKATCH-MODE COLD START
We use LLaMA-Factory for the cold-start fine-tuning. The LoRA rank batch size of 16 and a gradient accumulation step of 2. The learning rate is set to 1.0e-5, and the warm-up ratio is set to 0.1. We train for total of 10 epochs on the SketchColdStart-209 dataset. For the SketchThinker-P SketchThinker-B R133 we use Qwen2.5- -1-3----- as the backbone.
8, with training
we use Qwen2.5-V6 ----2-5-----B-IIIm Instrructt as the backbone, and for
D.2 SKETCHJUDGE REWARD MODEL
We also use LLaMA-Factor for fine-tuning our SketchJudge reward model, with Qwen2.5-7B Instruct as the backbone. The training batch size is set to 16. with gradient accumulation step of 8. The learning rate is set to 1.0e-5, and the warm-uu up ratio is set to 0. We train for total of 10 epochs. The training dataset for SketchJudge derived from SketchColdStart2200 For each question, there is an original long reasoning process and our constructed sketch-style reasoning process. A score of is assigned to the long reasoning process, and score of is assigned to the sketch-style reasoning process. As a result, we obtain fine-tuning dataset of 40K labeled data.
D.3 SKETCH- THINKING REINFORCEMENT LEARNING
We use Easy-R1 for the reinforcement training process. The base model for SketchThinker-R1- is the cold-started Qwen2.5-VI. --ee2.5- --------nttt model, and the base model for SketchThinkerr R1-3B is the cold-started Qwen2.5-Ve Owen2.5-VL-33-- VL-3B-Instrrct model. The training data, SketchRL-IK, is curated from MMStar, Math Vista. Visia and SeePhys. The maximum prompt length is set to 2048, and the maximum response length is set to 2048. We use adopt GRPO as the RL algorithm. The KL penalty is enabled with penalty coefficient of 1.0e-2. AdamW is used as the optimizer, with learning rate of I. 6.0e-6 and a weight decay of 1.0e-2. The rollout batch size is set to 512. The rollout sampling time is set 5, and the model temperature is set 1.0. We train for total of 15 epochs.
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D.4 TRAINING DETAIL OF BASELINE
All baselines share the same training data as SketchThinker-RI except for the prompt-based base- lines. We provide the details of constructing the training data for each baseline as follows:
SFT-based baselines. For C3oT, we leverage the same data samples as SkeachColdStart22K5 We directly utilize the original CoTs from LLaVA-CoT and Vision-RI-Cold as the long CoTs, and follow the data generation pipeline described in the C3oT paper to construct the corresponding short CoTs. In addition, we also generate long CoTs for SketchRL-IK with GPT-5, since the ground-truth long CoT's are missing for this set. We then produce short CoTss for those long CoT' following the data generation pipelines in C3oT paper. All these short and long CoTs are then mixed together to fine-tune the base model, ensuring fair comparison. For VeriThinker, we also utilize the same data samples in SketchColdStart-22 small non non-reasoning model, Qwen2.5- VVL-BB Instruct,, to generate short-CoT data for these samples. We also apply the same procedure to construct the short-CoT data for SketchRL- IK. All generated short-CoT data are then utilized to fine-tune the base model for the verification task, following the original training setup, to enhance its efficient reasoning capabilities.
as the data source. Following the original paper, we leverage a
RL-based baselines. For LI, utilize the same RL training dataset (SketchRL-IK) to perform reinforcement learning with the length- reward proposed in the original paper. For ThinkPrune, also use SketchRL-11K K for reinforcement learning and follow the response truncation and reward- shaping strategies described in the original paper.
E MORE ABLATION AND ANALYSIS
Ablation between binary reward and dense reward. We conduct an ablation study to compare binary sketch-thinking reward and dense sketch-thinking reward (see Tab. 4 Specifically, we im- plement dense reward by prompting Sketch/udge to output a floating-point score between 0.0 and 1.0 for thinking style of model, assigning higher scores to more sketch-style thinking and lower scores to more normal-style thinking. We perform this ablation on Sowen2.5-VL-7B-Innst evaluate on the MMMU benchmark. We find that the binary reward yields better results than the dense reward. We analyze that the binary reward providing much stricter and more direct supervi- sion signal during the reinforcement learning process. This direct supervision helps the model more effectively acquire sketch-style thinking ability and leads to improved performance.
and
Reward Design
Acc.t
#Token]
BoTt
Binary Reward
62.8
64.3
0.977
Dense Reward
62.6
65.4
0.957
Table 4: Ablation between binary reward and dense reward.
Ablation between weight of sketch-style thinking reward and accuracy reward. We present an ablation study between weight of sketch-style thinking reward and accuracy reward (see Tab. (5.. We gradually increase the weight of the sketch-style thinking reward while decreasing the weight of the accuracy reward. We observe that the weight ratio of 0.5 0.4 : 0.1 achieves the highest Efficiency of Thinking (EoT). As the weight of the sketch-style thinking reward increases from 0.05 to 0.1, we observe both reduction in thinking cost and improvement in answer accuracy. This is because ef- fective learning of sketch-style reasoning contributes to both improved thinking efficiency and more accurate question answering. As the weight increases continuously from 0.1 to 0.4, although the thinking token cost sees further reduction, accuracy also degrades, ultimately leading to suboptimal FOT. Too high weight for sketch-style thinking (e.g., 0.4) and too low weight for the accuracy re- ward (e.g.. 0.2) can lead to reward hacking, where the model focuses solely on achieving higher sketch-style thinking reward and neglects correctly answering the question.
Ablation between weight of sketch-style thinking reward and format reward. We further per- form an ablation study in which we gradually increase the sketch-style thinking reward weight while decreasing the format reward weight (see Tab. (6) We find that the weight ratio 0.5 0.4 0.1 also achieves the highest EoT across all settings. We observe a similar trend to the ablation between weight of sketch-style thinking reward and accuracy reward. We analysis that after Sketch-Mode
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Accuracy: Format Sketch
Acc.t
#Token
BoTT
0.55 0.4 0.05
62.2
65.2
0.954
0.5 0.4 0.1
62.8
64.3
0.977
0.4 0.4 0.2
62.3
63.9
0.975
0.3 ; 0.4 0.3
61.6
63.5
0.970
0.2 : 0.4 0.4
60.8
62.8
0.968
Table 5: Ablation between weight of sketch-style thinking reward and accuracy reward.
Accuracy: Format
Sketch
Acc.t
#Token
BoTT
0.5 0.45 0.05
62.5
64.8
0.965
0.4 0.1
62.8
64.3
0.977
0.5 0.3 0.2
62.5
64.1
0.975
0.2 0.3
61.8
63.8
0.969
0.5 0.1 0.4
61.2
63.5
0.964
Table 6: Ablation between weight of sketch-style thinking reward and format reward.
Cold Start, the model has already learned the correct response format and thus receives high for- mat reward. As a result, increasing the weight of the sketch-style thinking reward means relatively reducing the weight of accuracy reward. leading to similar performance trend as in the ablation between the weight of sketch-style thinking reward and accuracy reward.
Ablation of combination strategy of multiple rewards. For the combination strategy of multiple rewards during reinforcement training, in addition to the weight combination of accuracy, format, and sketch-style thinking rewards, we also experiment with more flexible strategies (see Tab. 77 The first is a staged weighting scheme for the sketch-style thinking reward. Specifically, we set the weights of accuracy, format, and sketch-style reasoning 0.45 0.40 0.15 during the first 30 steps, 0.50 0.40 0.10 during steps 30-60, and 0.55 0.40 0.05 during the remaining steps. The second dynamic weighting scheme. In this setting, we linearly decrease the sketch-style thinking reward weight from 0.15 to 0.05 over the entire training process according to the current step, while simultaneously increasing the accuracy weight from 0.45 to 0.55. We observe that: (1) The staged weighting scheme for the sketch-thinking reward achieves better results than the fixed- weight setting. We analyze that assigning higher weight to the sketch-style thinking reward the early stage of RL encourages the model more effectively transfer the sketch-style thinking ability learned during cold-start training to the new domain of RL training data. In addition, increasing the accuracy weight toward the end of RL training helps the model refine how it utilizes sketch- style thinking to obtain correct answers. (2) The dynamic weighting scheme for the sketch-thinking reward yields even better results than the staged scheme. We attribute this the smoother transition it provides, from emphasizing the generalization of sketch-style thinking (from cold-start to RL data) to prioritizing answer accuracy, ultimately leading more effective -ketch-thinking ability.
Sketch Reward Weight
Acc.t
#Token..
EoTt
Fixed Weight
62.8
64.3
0.977
Staged Weight
63.0
63.8
0.987
Dynamic Weight
63.2
62.5
1.011
Table 7: Ablation of combination strategy of multiple rewards.
Baseline optimized for absolute maximal accuracy. We attempt to establish accuracy baseline optimized for absolute maximal accuracy and present the results in Tab, g Specifically, we first fine- tune Twen2.55VL-77. Instruct the original long reasoning data of samples in SketchColdStart- 20K. Based on this cold-started base model, we further conduct RL training and tune several key RL hyper-parameters to seek for higher accuracy, including the learning rate, number of rollouts, and
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Method
Ir
n.rollout
kLooefficient
Accit
#Token
BoTt
Vanilla-R1-7B
1.0c-6
1.0e-2
61.0
182.2
0.335
Vunill--l-7B-CooddSuut
1.0c-6
1.0e22
61.5
211.3
0.291
Vanilla-R1-7B-Colae
5.0c-7
1.0c-2
61.6
208.3
0.296
Yamilla-RI-7B-ooldSae
1.0c-6
10
1.0e-2
61.5
205.8
0.299
V
1.0c-6
1.0e-3
62.0
218.4
0.284
SketchThhinke-RR1-7B
1.0c-6
1.0c-2
62.8
64.3
0.977
Table 8: Baseline optimized for absolute maximal accuracy.
SketchThinker-R11
Vanilla-R1-7B
SketchThinke--R1
Vanilla-RI-BB
ITraining Samples
Acc.t #Token]
EoTT
Acc.f
#Token),
EoTf
Acc.f
#Token]
EoTf
Acc.t
#Token]
EoTf
IK
62.8
64.3
0.977
61.0
182.2
0.335
55.9
54.5
1,026]
54.8
128.3
0.427
2K
64.5
60.1
1.073
62.3
185.5
0.336
57.2
52.3
1.094|
56.1
132.4
0.424
3K
65.2
58.3
1,118
63.5
183.9
0.345
57.8
51.4
1.125|
56.8
133.8
0.425
65.8
57.5
1.144
64.2
188.9
0.340
58.3
50.8
1.148|
57.6
134.5
0.428
5K
66.1
56.8
1.164
64.6
192.1
0.336
58.5
50.3
1.163|
57.8
133.2
0.434
Table 9: Scaling experiment for the size of RL training set.
SketchTTinker-RI.
Vanilla-R1-7B Acc.t
SketchThinker-RI
Vanilla-RI-3B
#Training Steps
Acc.t #Token.
EoTt
#Token.
EoTt
Acc.t #Token., EoTT
Acc.f #Token.).
BoTt
100
62.8
64.3
0.977
61.0
182.2
0.335
55.9
54.5
1.026]
54.8
128.3
0.427
200
63.2
62.1
1.018|
61.4
185.5
0.331
56.3
$3.4
1.054]
55.2
132.5
0.417
300
63.4
61.8
1.026|
61.2
186.2
0.329
56.5
53.1
1.064
55.0
131.2
0.419
400
63.2
61.5
1.028
61.5
185.8
0.331
56.2
53.4
1.052
55.2
133.8
0.413
500
63.5
61.6
1.031
61.2
185.3
0.330
56.4
$2.9
1.066
55.3
133.5
0.414
Table 10: Scaling experiment for the number of training steps.
the coefficient of the KL penalty. We refer this model achieves higher accuracy of 61.8 after an initial fine-tuning stage
We observe
that Vanilla-R1-7B-Colddee on long CoT data and careful hyperparameter tuning during reinforcement training. However, its Moreover, because this baseline
performance is still lower than that of our SketchThinker-R11-7 cold-started with long reasoning data, thinking cost increases significantly. This illustrates the effectiveness of sketch-style reasoning in reducing thinking cost while contributing accurate question answering.
SCALING EXPERIMENT
We conduct two scaling experiments: (1) scaling the size of RL training set. (2) scaling the number of training steps.
Scaling the size of RL training set. We conduct data scaling experiment with RL training sets of size 1K, 2K, 3K, 4K, and 5K (see Tab. D Our RL training data is drawn from MMStar, Math Vista, VogicVista, and SeePhys, resulting in training pool of 9,088 examples in total. We randomly sample 2K, 3K, 4K, and 5K subsets from this pool and conduct a data scaling experiment. We conduct this experiment with both cold-started (wwen..5VLL-7B-Mn) VL-3B and evaluated on MMMU. We observe that both SketchTTinker-RI- R1-3B scales well with increased RL training set size. As the number of RL training samples increases from IK to 5K, the accuracy of our method gradually improves, while the token cost steadily decreases, leading improved FoT (Efficiency of Thinking). Moreover, SketchThinker
and cold-started Qwen2.5- and SketchThinker.
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RI consistently outperforms Vanilla-RI in terms of both accuracy and thinking cost across all data scales.
Scaling the number of training steps. We also conduct scaling experiment the number of RL training steps (see Tab, 9). Specifically, we conduct 500 RL training steps on IK samples with cold- started Qwen2.5-VIL -L-----------nttruc and cold-started Qwen2.5-VL-3B. We save checkpoints 100, 200, 300, 400, and 500 steps, and evaluate each on MMMU. We observe that SketchThinker-R exhibits stable training dynamics increases and then plateaus, while the thinking cost gradually decreases and then stabilizes. There are no signs instability during RL training for our method, such as sudden drops in accuracy or RI maintains clear margin over
scale the number of training steps. The aceuracy gradually
thinking token cost collapsing to zero. In addition, Vanilla-RI terms of both accuracy and thinking cost as the number of training steps increases.
G INTERPRETABILITY OF SKETCHTHINKER-RR REASONING TRACE
Human study. We conduct human study evaluate the interpretability of the SketchThinker RI reasoning traces (see Tab. [T]. Specifically, we randomly find 5 human evaluators to perform the assessment. We sample 5 questions from each of MMMU, Math Math VisuLogic, and PhyX that are correctly answered by both SketchThinker-RI and Vanilla-R1, resulting in 20 samples in total. We present the original question, image, and the reasoning traces from both models to the evaluators. The evaluators are asked to assign a score of 0, 1, 2, 3, 4, or to the reasoning traces of SketchThinker-Rl and Vanilla-R1, where a higher score indicates better interpretability. achieves a higher interpretability score than Vanilla-R1. Sketch-style rea- soning is concise, and clearly presents the key logical steps for problem solving, making much casier to follow than long, verbose reasoning traces. As result, SketchThinker-R attains better interpretability scores than Vanilla-RI.
Method
Avg. Interpretability Score
Vanilla-R1-7B
3.95
SketchThhineerRR1-BB
4.25
Table 11: Results of human evaluation on the interpretability SketchThinken reasoning traces.
Large-scale LVLM-based Evaluation. Since the human study is based on a small sample size, we further conduct a large-scale LVLM-based evaluation of the interpretability of the reasoning traces produced by our model (see Tab. 12 Specifically, we utilize Qwen3-VL-Plus as the evaluator. We collect all samples from the four evaluation benchmarks (MMMU, Math Vision, VisuLogie, PhyX) where both KkeecchThinkr-RRI--1 Owen3-VL-PPar assign a score in range (0, 5.0) to the reasoning traces of both SketchThinker-P 7B and Vanilla-R1-7B3 where a higher score indicates better interpretability. The LVLM evaluation results are consistent with the human study, showing that our method achieves higher interpretability scores than Vanilla-R1. This further demonstrates the high interpretability of the reasoning traces produced by our trained model.
and Vanilla-RI R1-BB generate the correct answer. We then prompt
Method
Avg. Interpretabilitty Score
Vanilla-R1-7B
4.12
(NetchTThinker-1111
4.33
Table 12: Results of LVLM-based evaluation the interpretability of SketchThinker-RR rea- soning traces
H DATA QUALITY OF SKETCHCOOLSSTARI-00K
To illustrate the quality of our generated sketch-style reasoning data, we analysis from three aspects: (1) human study, (2) LVL. evaluation, and (3) case study.
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Human study. We conduct human study to evaluate the quality of our generated sketch-style reasoning data (see Tab. 13 Specifically, we randomly find 5 human evaluators to assess our gen- erated sketch-style reasoning data, focusing whether the key reasoning steps in the original long reasoning are preserved. Specifically, we randomly select 20 samples from SketchColdStart-200 for evaluation. For each sample, present the evaluator with the original image, question, long reasoning, and the generated sketch-style reasoning. The evaluators are asked to judge whether all necessary steps included in the sketch-style reasoning and assign score of 0 and where indicates that all necessary steps are preserved and O means some key steps are missing. We ob- serve that Evaluator #1 and Evaluator #3 assign a score of all randomly sampled cases from SketchColdStart-20k For the other evaluators, most samples are also given score of 1. This con- firms the high quality of our generated cold-start data from human perspective, which we attribute to the strong capability of GPT-5 for text-related operations.
Score
Evaluator#1
Evaluatori2
Evaluator#3
Evaluatorti4
Evaluator#5
20
19
20
18
19
Table 13: Results of human study on quality of our generated sketch-style reasoning data.
LVLM-based evaluation. To further assess the quality of our generated cold-start data, we also conduct a large-scale LVLM-based evaluation on all samples in SketchColdStart-25 (see Tab. [4]. Specifically, we utilize Owen3-VL-Plus image, original long reasoning, and the generated sketch-style reasoning. We prompt it to judge whether the generated reasoning process contains all key steps from the original long reasoning, and to assign a score of 0 or 1, where I indicates that all key steps are included in the sketch-style reasoning and indicates otherwise. We observe that, for most samples SketchColdStar-22K Qwen3-VL-Plus VL-Pllu judges the sketch-style reasoning to preserve all key steps from the original long reasoning. This illustrates the high quality of our generated cold-start data more systematic way.
the evaluator and provide it with the original question,
Score
Score Count
818
19182
Table 14: Results of LVLM-based evaluation on quality of our generated sketch-style reasoning data.
Case study. We also present several cases from SketchColdStart-001 for straightforward illustration (see Fig. 9 and Fig. [O). Our generated sketch-style reasoning data preserves all the key steps from the original long reasoning process.
I MORE DISCUSSIONS
Can we embrace such condensed, sketch-style reasoning data from the pretraining phase to significantly reduce training costs? Yes, we can. The significantly shorter length of sketch-style reasoning data can help reduce training costs in the pretraining stage. Shorter reasoning chains di- rectly lower the number of tokens per training sample, which reduces compute usage for both the forward and backward passes. At the same time, sketch-style reasoning data avoid redundancy in the reasoning process and therefore have higher information density. As result, the model is exposed to more distinct reasoning patterns per unit of compute, which is highly desirable at scale. Moreover, because sketch-style reasoning focuses on the key logical steps needed to solve a problem, it also substantially contributes to obtaining correct answers and improving model performance. In addi- tion. recent models such as GPT-OSS adopt a low-thinking mode that produces concise reasoning; think that such models could already benefit from sketch-style data during pretraining.
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Method
Training Time
Vamilla-R1-7B
2.70h
SketchThiinke-RI-77B
2.21h (x0.81)
Table 15: Comparison of training times between
and Vanilla-R1,
J TIME COST ANALYSIS
Inference time. We present the inference time cost of SketchThinker-RP and Vanilla-RI across different benchmarks (see Fig. IT). We observe that SketchThinker-F RI exhibits much faster response time. This is attributed to its sigmificantly reduced thinking token cost.
Training time. We also present the training time comparison between SketehThinker-II and Vanilla-R1 (see Tab. 15). The training time of SketehThinker-R1 is also shorter than that of Vanilla- RI, with reduction around 20%. This improvement stems from the much faster rollout process of Theckerri during reinforcement training. All training-ting time measurements are conducted on H800 (80G) GPUs.
K
USE OF LLMS
employ LLM during the paper writing stage, and its primary role is to assist revising the manuscript. In particular, the LLM is used to polish the English text and improve the coherence and clarity of the narrative. No part of paper is generated from LLM, ensuring that the core scientific contributions remain entirely the authors.
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Prompt for Converting Long Reasoning Sketch-Style Reasoning
You reasoning compression assistant. Your task is transform long, detailed reasoning into ultra-short, sketch-style reasoning as numbered list.
Instructions:
1. Read the long reasoning carefully.
2. Keep only the key facts and logic.
3. Remove all extra words, details, and examples.
4. Keep reasoning order intact.
5. Each step must extremely short.
6. Output numbered steps (1., 2., .....
7. Steps should still form clear logical chain to the conclusion.
Example:
Input reasoning:
The highlighted country within Pacific Islands region. Based its position relative neighboring larger landmasses like Australia and nearby countries such as Papua New Guinea and New Zcaland, the highlighted country aligns with the location of Vanuatu. According to the context, Vanuatu has territorial dispute over Matthew and Hunter Islands, claimed by both Vanuatu and France. Therefore, the presence of dashed box labeled "Disputed island" suggests the inclusion of this dispute in the overview of country's 's territories. Output:
1. In Pacific Islands.
2. Near Australia, Papua New Guinca, New Zealand
3. Likely Vanuatu.
4. Dispute with France.
5. Dashed marks dispute.
Task:
Convert the following reasoning into sketch-style reasoning: (reasoning)
Table 16: The prompt for converting long reasoning process into sketch-style during the construction of SketchColdStarr-0KK
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Question: In the figure, AB is the diameter of semicircle o, C is a paint on circle o, OD BC at AC:8C 4:3 and 10 cm, then the length of OD is 0 2 cm B.
<think>1. AB is diameter. 2. Angle ACB 90", 3. AC:BC 4:3, 4. AC 4x, BC 3x. 5.
5x by Pythagoras. midpoint of OD 1/2 AC
cm.
10, AC cm.
10. cmc./hintbbcnneeeesttssswee
Question: As shown in the figure, in the parallelogram ABCD, AE evenly divides 2 BAD and intersects at E. AD 3, AB 5, then this length A. B. 2 3 D.
D
E
<thinko Opposite sides equal, parallel.
AB
CD. bisects
/ BAD. S. DAE LEB. 6. LEAB LDEA. 7. DAE /DEA. 8. Triangle ADE
isocceles. 9. DE AD 3. 10. EC CD DE. 11. EC 2./ttinnsscmmweeek//mwee
Which property matches this object? Context: Select the better answer. Options: (A) breakable (B) soft
Question:
<thinko Plate ceramic. 2. Ceramic hard, brittle. Image shows breakability. Not soft, pliable. 5. Breakable when dropped. 6. Answer: (A)
breakable. htiteettiiitttassssrrninnnn Answer:Ac/answer answers
ceramic plate
Question: Which kind
of keyboard?
<thinko Office desk image. Dual monitors. Printer left. 4. Crackers left keyboard. Keyboard center. right. Telephane far right. Crackers rectangular, 9. No other food shown. 10. Answer: Crackers. waakkeess./hhiinccoocc thinoooansserrFFind AnowerCrraceess//nnwerr
Figure 4: Examples of Sketch data. Our sketch-style reasoning process focuses on key cues for solving questions. The thinking process effectively contributes to obtaining correct answers. At the same time, the thinking process is very concise, which significantly reduces the thinking cost.
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Lig + > LLi
Question: What does the represent the nuclear reaction A: alpha particle 8: an electron C: a neufron D: proton
represents the mass and charge. The reaction is: [ [f"6texH(Ujj_3+ rightorrow ("7text(L)__3 ] The mass numbers the left side 6 and on the right side are 7, 5o the mass number of the unknown particte must be (since 6 = 7). The charge numbers are on both sides, so charge of the unknown particle must The options provided are: an alpha particle (mass charge +1) The only particle that fits the mass and charge criteria is neutron. Therefore, the represents neutron. </ </think> canswersC<//rme answer>
Vanilla-RI:
<thinks determine
need consider the
electron (mass charge -1) neutron (mass 1, charge 0)
(mass charge
Thinking Token:
SketchThinker-Rl <thinlo-1. changes
charge changes Add nucleon Neutron adds one mass No other particle adds mass 6. Therefore, neutronc/ poutrron//hinoccccsseec////aa/
Thinking Token: 54
Question: In the diagram below, overline(P S) tangent to circle at point overlinefP a R) secant, P Sex, P Q-3, and Rax+18. What is the length overline(p 5} A: B: 9 D: 27
Vanilla-RI: <thinks To solve this problem, we use the Power of Point theorem, which states that for a point (P) outside circle, the power of the point cdot PR Given:
same for any secant or tangent drown from (P
circle. theorem
expressed into the Power of Paint formula: xx'2 3 adot (x 18) Simplify the equation: [ [x"2 3x 54 Rearrange the equation to form a standard quadratic equation: [ [x"2 3x 54 0 Factorize the quadratic equation: [ (x 9)xx 6) =O] gives two possible solutions:
PP'"2
18 Substitute these values
quad text(or) quad 0] [x quad text(or) quad Thus, the length of overtine(PS) 9. thinks
Since represents length, must positive. Therefore, cantwers8c/answer.
Thinking Token: 285
SketchThinker-R1 cthinko tangent at S, PQ+QR secant 2. Power point: PS* PQ-PR 3. Substitute given values:
3-(x+18) Expand simplify equation quadratic equation Discard
Length
9</ hhhhinooonssweeeB/aaasae
Thinking Token: 82
Figure 5: Qualitative case of Sketch Thinker-RL..
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Question: If the Initial speed of the stream is ( at what height does the water strike the building? A: (d tan theta_i fracigd"" 2Her_ cot"2 theta_ ()) B: ( tan
theta_j- fracigd 2H2v_ tan"2 theta_]) tan theta_] Fracfgd"2H2v_J 2H2v_ theta_]) ( theta_i 2aav sin theta_i))
Vanilla-R1: ethinks To determine the height projectile tile motion find the vertical height at which the water hits the building. The harizontal distonce (d v_i cos theta_i cdot 1 where (f is the time of flight. The vertical height (h can be expressed as: [ v_j sin theta_i cdat t- frac(1H2) g t*2 We can salve theta_i) Substitute
which the water strikes the building, we need to analyze the stream. The harizontal distance (d and angle theta_i ) are given, we need
be expressed
(+) from the horizonfal distance equation: I fracfdHy cos theta_i fracidHy cos theta_i frac(1H22
vertical height equation:
left( fracldHy cos th right)" [[ d fan theta_i frac(1H2) frackd"2Hv cas"2 theta_ ] [h tan theta_i frac(gd 2Hav_jj22 cos theta_i] Therefore, the correct answer is C. </thik canswersGe/ answers
Thinking Token: 282
SketchThinker-RL <thinko Horizontal distance horizontal velocity
displacement initial vertical velocity transfer time (ravitys time)*/2 Horizontal velocity v_j cas(e_i) Transfer time ddv_jrcas(@_i) 5. Vertical displacement habd//_j_gdd//_cccre 6. vertical displacement
(rnn(e_))gdd+/2(y___ (ow_j_cco(B___p) cainlo Co/owewr>
Thinking Token: 130
Question: What is the magnitude of the radial acceleration of a particle in the corresponding uniform circular motion? A: 1.3 times 10"3 m/s"2 1.5 times m/s"2
10"3 m/s"2
m/s"2
Vanilla-RI: cthinks determine magnitude need to the formula for radial acceleration, which given dar (nac)"2((r) where ( the velocity of the particle and (r) rr) the rodius of the circular path. From the graph, we can see that the amplitude of the motion is cm, which means the radius (r) of the circular path cm 0.08 m. The velocity (v) can be found from the slope the the
particle
derivative of position with respect to time. The maximum velocity v_{text(mxxi) ) can be found considering the maximum slope of the graph, which corresponds to the maximum velocity. The maximum velocity ( v_xttxx(maxxit can be calculated as: [__[tex((max]] fraeffexfammituudeelfeeffff
tangent any point the graph, but
the velocity
period} 2) From the groph, the period ) approximately ms (or 0.02 s). Therefore, the maximum velocity (texxx((axx)) frac(8 textf cm)Ho.02 text( Frac(0.08 text( m)Ho.02 textf s)} 4 textf m/s) ] Now, we can calculate the radial acceleration: aur (taxfx_(ax 2)(r) frac|(4
textf m/s))" 2)(0.08 texts m]} fracf16 textf m)" (/2//xxt(s)" 2)/0.08 textf mi} 200 textf m/s)"2 This value closest to
( times 10"3 textf m/s]"2 Therefore,
</thinko canswersCe/ answer>
Thinking Token: 433
SketchThinker-RL. cthinko Identify A radius, 8 0.08 Determine period from graph Compute angular speed 2n// acceleration (2m/n)² given numeric value Match value hhhan//hiikkccnswer////aawwww
Thinking Token:
Figure Qualitative case of SketchThinker-
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Question: Divide following six figures into two categories, so that each category of classification (8@)
figures
feature or pattern.
3 D30 C40 C: D&G 234 Dr D3Q, $$$
Vanilla-R1: <thinks To be used to divide them into two categories. Let's analyze each figure: 1. Figure (D: It has pattern of triangl es and square. 2. Figure @:: It has pattern of triangles and triangle. 3. Figure (:: It has a pattern a square and square. Figure (f:
problem, need identify
feature pattern among the figures that
pattern (): It has pattern of square and square. From the analysis, pattern involving square, while figures @. @ and ( do not have square in their patter.. Therefore, the correct classification is: (100)) @40 </thinko sanswer>De/ answer>
and square. Figure
has pattern
square and square. Figure that figures (1) (3), and all have
Thinking Token:
SketchThinker-RL cthinko 1. Analyze shope presence/ arrangement D-C have lines, shopes configunation 4. @ only lines present 5. So categories: shapeselines, lines, lines-only:/ aels--onyy/hhinnocoreeeeeee tf answer>
1SS shape-line
Thinking Token:
?
Question four given options, the suitable the place of the question mark, that It presents certain regularity: A: A B D.
Vanilla-R1: <thinks To determine the correct answer, let's analyze the patter the sequence of shopes: 1. The first shape a circle divided into two equal parts. The second shape circle divided into four equal parts with an X shape circle
fourth shope circle divided into eight equal parts. The pattern seems to be increasing the number of equal parts in the circle. Following this pattem, the next shape should be circte divided into ten equal parts. Now, let's examine the options: A: circle divided into five equal parts. circle parts. The correct option that fits pattern SnntwersDD/anwwers
equal parts.
four equal parts. C: divided into six equal parts D: represents circle divided into ten equal c/thinks
divided
equal
Thinking Token: 193
SkefchThinker-R1 cthinko add per shape shape:
successively
shope: two lines Third shape: three lines 6. Fourth shape: four lines 7. Next shape: five lines 8. Select shope with five linesc/ fines//tinboccssweery waiibe-aasserrDD/aaarre D</aweerr
Thinking Token: 73
Figure 7: Qualitative case of SketchThinker
25

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lateral Published as a conference paper at ICLR 2026
Question: As shown figure, then the bottom area of cone 0 Cholces: A:31Tem ((2) 8:9mem"(2) ((2 C:16mrmm D:25trem ((2)
Vanilla-R1: <thinks To find the bottom area of the cone, we need use the formula for the area of circle, which pi figure,
r'2 ), where (r) rodius the
that
height
the Pythogorean theorem to find the rodius (r) of the base of the The Pythagorean theorem states: [r"2 h22 2 where (r) is the rodius, (h is the height, and (1 is the slant height.
5 and the height
We
Substituting the given valuees: [r"2 + 4 5*2 (22 25 [r"2. find
sqrt(9) [ 3 pi times 3 9pi Therefore,
16 [ r
the the cone: text(Area)
the is ( cm² </ e/thinlo canswers answwrrr>>
Thinking Token: 267
SketchThinker-R1: <thinks1.
~((4 39) 2. r-+ cm
Area nr m-3= 9m hwn.: cmtc/hinkoxxnsserrBBBcc answer>>
# Thinking Token: 48
Question: As shown in the figure, rohate triangle ABC around the line where AC is located to obtain rotating body, then the lateral area of rotating body Choices: A:12m
Vanilla-R1: ethinks To find the lateral of the rotating body, need to recognize that rotating triangle ABC around the line where AC located will form cone. The lateral area of cone is given by the formula ( pi r ). where ( is the radius of the base and (1 is the slant height. From the figura, we can see that the rodius (r of the base of the length BC, sqri(Ac"2 BC"2) sqrt(4"2 3"2) 5 )). So, the lateral area of the cone is: A 15pi Therefore, the lateral area of the rotating body is ( 15pi ). </ //tnnnk canswer>8</ aanswerr
height (1 the
is length AB,
(since times times
Thinking Token: 201
SketchThinker-R1 1think hypotenuse, so I+5 2. Lateral mT-I-c Substituting: m-5-3 15m area is 15ttt/tinkkoeeeee www.h//wwcnsweee/e//eeeeeeeeeeee
Thinking Token:
Figure 8: Qualitative of
26

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Published as a conference paper at ICLR 2026
Question: As shown in the figure, A8 parallel to CD, and AC infersects BD at point O. If AO 3, 80 6, Co 2, then the length of BD is A. 10 11 D.
applies. According to this theorem, since A8 parallel CD, the ratios the segments created by their intersection with the transversal lines are equal, Le., Vfrac(AOHOC)= \frac(BOHOD): Given: AO 3, 80 6, CD = We need to find DO. By setting up the proportion based on the theorem: \fracAAOHOC] \frae{BOHOD) \Rightarrow \fracf3H2) = \frac(6]DD) Thus, length segment
Original Long Reasoning: <thinkoGiven
parallel
O, intercept
Cross-mul tiplying to solve 10.4/ http://nnnnwweeeee//nneeoaoe
Atimes Atimes Atimes
BD BO DO
Generated Sketch-style Reasoning: cthinko1. parallel to CD. AC intersects
Intercept thearem applies.
4. Ratias equal: \frac(AOHOc)- \frac(BOHOD). 5. Giver: AO 3, 80 Co 6, Set up proportion: \frac(3)2] \frac(6HDO). 7. Crass-multiply: Viimes DO 12. 8. Solve: DO 4. 80 DO h0.c/hhinkoocnnseerBB/ answer>
Question: the figure, point is an side BC of triangle ABC, O the center, circle point tangent () 65' B. 50 c. 40" 30
point the AB, 60", C
B
Original Long Reasoning: cthinkoSince LADB is an inscribed angle that intercepts arc AB, we can use the property that angle center
makes central angle ZAOC 180" LAOB 60", the angle around point 360". shared equally both sides AOB and AOC. Since AC is tangent to the circle at A, angle L OAC is 90" due to the tangent-segment theorem, which states that the tangent at any point of circle forms right angle with the radius to the point tangency. Thus, to find LC, we use the triangle
inscribed angle. Therefore, LAOB 2//DBB
180 gives 90 60' LC 180 Thus, /C 180* 150* 30" :/thinnkaaanswerrDD 30" </answerrr>>
the point triangle AOC, which
Generated Sketch-style Reasoning: cthinko1. ADB intercapts are AB. LA08 2 120". LAOC 180" 120" 60 tangent 30" "/..//inoccccswers.. 30" anaswer>>
90" /OAG /AOC /c=
60" - 180". LC=
Figure 9: Illustration of our generated sketch-style reasoning data and the corresponding orig- inal long reasoning data.
27

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Published as a conference paper at ICLR 2026
Question: Think about the magnetic force between the magnets in each pair. Which of the following statements is true? Context: The images below show two pairs magnets. The magnets different pairs do not affect magnets (A) The magnetic force is weaker in Pair (B) The magnetic force weaker In Pair 2. (c) The strength of the magnetic force is the same In both pairs.
made the material. Options:
Original Long Reasoning: cthinkkaagnettic force between magnets depends magnets are, the stronger the magnetic force, and the farther apart they are, the weaker the force. In Pair 1, the magnets are inches apart, and Pair 2, they are inches apart. Since Pair 2 has a larger distance between the magnetic force
distance between them. The clater the
magnets, magnetic force in weaker Pair 2.</ 2.c/arswe>>
compared to L<//hinkoxansseerx(8
Generated Sketch-style Reasoning: <thinkol. Magnetic force depends on distance. 2. Closer magnets, stronger force. 3. Farther magnets, weaker force. 4. Pair inches apart. 5. Pair inches apart. Pair weaker than Pair Lc/thinkoxanswerr(B The magnetic force Pair 2.</answer>
Question: Which property do these three objects have in common? Context: the onswer. Options:
(B) soft (c) yellow
Original Long Reasoning: <think>To determine what property these objects share, will evaluate each option: Option (A) hard: A hard object doesn't change shape when pressed. Track sults, aprons, and handkerchiefs are typically made of fabrics that are flexible, not hard. Option (8) soft: Soft objects change shape when pressed. three Items, being made fabric, soft flexible. Option yellow: Yellow color, and yellow. Based the properties, the shared characteristic among the track suit, and handkerchief is that they are all soft.</ htt.:///hikkkasee/////sssss
these objects Indicated
Generated Sketch-style Reasoning: cthink> Evaluate each option. 2. Hard: Unchanging shape. Track suits, aprons, handkerchiefs Reexible. 4. Soft: Changes shape. 5. All items soft, Rlexible. 6. Yellow: No indication. 7. Shared: Soft. httt::/hinnn.csseee///////////
Illustration of our generated sketch-style reasoning data and the corresponding original long reasoning data.
Figure 10:
Sketchhhhnner-R117
Vanilla-RI-P
Inference Time (minutes)
100
50
MMMU
Vathiissoon
VisuLogic
PhyX
Benchmark
Figure 11: Inference time of SketchThinkeen and Vanilla-RI across different benchmarks.
28


Reviews:
[{"review_id": "ZZJSzVJvJW", "reviewer": "Reviewer_FKy6", "summary": "The paper introduces SketchThinker-R1, an RL framework that trains multimodal LM to elicient concise reasoning instead of verbose ones, while keeping the same level of accuracy. The method contains three steps. The first step is use a strong LLM to converte existing long COT reasoning traces into short ones with numbered steps, and then do cold-start SFT. The second step is to train a SketchJudge reward model that can classify if a reasoning trace is \"sketch style\". The third step is do GRPO with the verifiable reward, format reward, and the sketchjudge reward.\n\nThe authors experiment this method on MMMU, MathVision, VisuLogic, PhyX, and show that this method can reduce reasoning tokens by >64% while keeping the accuracy.", "questions": "1. About the reward model SketchJudge. What alternatives have you tried? Like for example, a dense reward. Why using a binary reward in the end?\n\n2. How is the weight 0.5, 0.4, 0.1 determined? What will happen if using other weights. Does the SketchJudge hurts accuracy if the weight is too big? Is there other method to combine these multiple objectives?\n\n3. Have you run a human study on the interpretablity of the reasoning traces of the models you trained? Are the reasoning traces concise and reasonable, or they are just shorter but with many unreadable stuff?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "1. The method gives good efficiency gains on multimodal reasoning, with 64% token reduction, while keeping the accuracy.\n\n2. The method is simple and make sense. It shows that by carefully curate the SFT cold start data, and a new reward for sketch-style, it is possible to achieve similar reasoning process while greatly reduce the token count.\n\n3. The ablations are well designed and provide good analysis.", "weaknesses": "1. The major weakness is that the scale of the experiments are very small. It only contains 1K prompts training set and 150 steps. It is questionable whether this method is scalable and generalizable on more tasks. What will happen if a bigger training set is used, and more FLOPs trained? Will it be continually improving, or this method is more unstable compared with vanilla R1? The paper would be stronger if include more scaling experiments.\n\n2. More analysis on the reward models would make the paper stronger. Now the method uses a 0/1 reward on the \"sketch\" style. What if we use a dense reward? Also, it has a weight of 0.1. What will happen if we use other weights? Any reward hacking, or competing with other rewards observed during experiments?", "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": "4T0MKC5Vit", "reviewer": "Reviewer_ag5r", "summary": "This paper proposes SketchThinker-R1, a three-stage framework to train large multimodal models to produce “sketch-style” reasoning chains: first a cold-start stage where long form reasoning traces are converted into concise sketches and the model is fine-tuned on them; second a SketchJudge reward model is trained to explicitly score reasoning traces by favoring the sketch style; third a reinforcement-learning (RL) stage (using GRPO) encourages the model to produce sketch-style reasoning under the reward model’s supervision. Their motivation is that current large multimodal reasoning models use long chain-of-thought traces, which incur high token cost and increased latency, while human “sketching” uses fewer but more salient steps — thus they aim to reduce reasoning token cost without sacrificing accuracy. They build a dataset “SketchColdStart-20K” by converting 20K long-form reasoning traces (10 K from each of two multimodal reasoning datasets) into sketch‐style reasoning via a strong LLM (GPT-5) and then fine-tune the base model. They then sample a small RL set of 1 K from four domains (MMStar, MathVista, LogicVista, SeePhys) for the RL stage.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The motivation is good. Reducing token number is reasonable for multimodal tasks thinking. \n\nThe three‐stage pipeline (cold‐start conversion of reasoning style, reward model, RL) is reasonably well‐designed and coherently described. The reward model is reasonable.\n\nExperiments consistently show good results spanning different domains (visual reasoning, logic, physics) while showing substantial reductions in token cost while preserving good accuracy.", "weaknesses": "1. Suggest an experiment on ratio of the sketch reward. What if increasing the sketch coefficient and lower the format coefficient?\n\n2. It is unclear if every baseline shares the same training data or just vanilla r1. Good to include more details.\n\n3. How much time (GPU hour) in practice could be saved?", "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": "trAkYUTdX2", "reviewer": "Reviewer_5YsX", "summary": "This paper presents SketchThinker-R1, a reinforcement learning framework designed to induce sketch-style reasoning in large multimodal models—defined as concise, goal-directed reasoning that retains essential logical steps while eliminating redundancy. The method is composed of three stages: (1) Sketch-Mode Cold Start, in which long-form reasoning traces from LLaVA-CoT-100K and Vision-R1-cold are systematically distilled into sketch-style sequences via GPT-5, followed by supervised fine-tuning to instill initial reasoning condensation capability; (2) SketchJudge Reward Model training, where an open-source LLM (Qwen2.5-7B-Instruct) is fine-tuned on paired reasoning samples (normal vs. sketch-style) to learn a reliable binary score for reasoning style, with no reliance on absolute length thresholds; and (3) Sketch-Thinking Reinforcement Learning, wherein the cold-started model is further optimized via GRPO, with rewards composed of accuracy (50%), format adherence (40%), and SketchJudge style score (10%). Evaluated across four standard multimodal benchmarks—MMMU, MathVision, VisuLogic, and PhyX—the method achieves an average reduction of 64.3% in reasoning token cost compared to the Vanilla-R1 baseline, with no degradation in answer accuracy and consistently superior efficiency.", "questions": "Considering the findings and conclusions presented in this paper, does this imply that we could embrace such condensed, sketch-style reasoning data from the pretraining phase to significantly reduce training costs?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper introduces a novel and comprehensive three-stage framework, SketchThinker-R1, which moves beyond superficial reasoning length constraints by directly fostering an \"intrinsic sketch-style thinking\" within large multimodal models. This approach represents a significant paradigm shift from external compression to internal cognitive efficiency.\n\n2. The development and integration of the specialized SketchJudge reward model are a key strength. This model's ability to accurately evaluate \"reasoning style\" rather than merely length provides a precise and stable signal for reinforcement learning, enabling the model to learn context-aware reasoning condensation.\n\n3. The framework consistently achieves substantial reductions in reasoning token cost (over 64% on average) across diverse benchmarks (MMMU, MathVision, VisuLogic, PhyX) while maintaining or even improving answer accuracy. This demonstrates a robust solution to the critical challenge of high computational inference costs in advanced multimodal models.\n\n4. This paper benefits from clear writing, well-structured sections, and effective use of visual aids. Specifically, Figure 1 (quantitative results overview) and Figure 3 (qualitative reasoning examples) are particularly effective in conveying the core idea and the method's impact, making complex concepts accessible to the reader.", "weaknesses": "1. This paper relies on a powerful closed-source large language model for generating concise \"sketch-style reasoning\" data. It remains uncertain whether these generated data consistently encapsulate all necessary reasoning steps, which could potentially impact the interpretability of the subsequent black-box reasoning model and challenge the paper's assumption of mimicking human thought.\n\n2. This paper claims to enhance efficiency without compromising accuracy; however, its accuracy baseline for comparison does not represent a model optimized for \"absolute maximal accuracy.\" If a model, trained solely on the original long reasoning data (with consistent samples but without prior condensation), could achieve higher accuracy, then SketchThinker-R1 might be achieving its efficiency gains at a suboptimal accuracy ceiling, suggesting an unexplored trade-off between accuracy and efficiency.", "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)