Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search
Authors: Xin Lai
Junyi Li
Wei Li
Tao Liu
Tianjian Li
Hengshuang Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: Visual Search;Thinking-with-images;Reinforcement Learning;
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advances in large multimodal models have leveraged image-based tools with reinforcement learning to tackle visual problems. However, existing open-source approaches often exhibit monotonous reasoning patterns and allow only a limited number of interaction turns, making them inadequate for difficult tasks that require trial-and-error exploration. In this work, we address this limitation by scaling up tool-based interactions and introduce Mini-o3, a system that executes deep, multi-turn reasoning—spanning tens of steps—and achieves state-of-the-art performance on challenging visual search tasks. Our recipe for reproducing OpenAI o3–style behaviors comprises three key components. First, we construct the Visual Probe Dataset, a collection of thousands of challenging visual search problems designed for exploratory reasoning. Second, we develop an iterative data collection pipeline to obtain cold-start trajectories that exhibit diverse reasoning patterns, including depth-first search, trial-and-error, and goal maintenance. Third, we propose an over-turn masking strategy that prevents penalization of over-turn responses (those that hit the maximum number of turns) during reinforcement learning, thereby balancing training-time efficiency with test-time scalability. Despite training with an upper bound of only six interaction turns, our model generates trajectories that naturally scale to tens of turns at inference time, with accuracy improving as the number of turns increases. Extensive experiments demonstrate that Mini-o3 produces rich reasoning patterns and deep thinking paths, effectively solving challenging visual search problems.

MINI-O3: SCALING UP REASONING PATTERNS AND INTERACTION TURNS FOR VISUAL SEARCH
Xin Lai
Junyi Li
Wei Li
Tao Liu
Tianjian Li
Hengshuang Zhao
ByteDancc
Thhe University of Hong Kong
ABSTRACT
Recent advances in large multimodal models have leveraged image-based tools with reinforcement learning to tackle visual problems. However, existing open- source approaches often exhibit monotonous reasoning patterns and allow only a limited number of interaction turns, making them inadequate for difficult tasks that require trial-and-error exploration. In this work, we address this limitation by scaling up tool-based interactions and introduing Mini-o3, a system that executes deep, multi-turn sowoonnn-spannene tens of anepsand achieves oftte-of-thharrt performance on challenging visual search tasks. Our recipe for reproducing Ope- nAI 03-style behaviors comprises three key components. First, we construct the Visual Probe Dataset, collection of thousands of challenging visual search prob- lems designed for exploratory reasoning. Second, we develop an iterative data col- lection pipeline to obtain cold-start trajectories that exhibit diverse reasoning pat- terns, including depth-fine first search, haul-and-error. and goal maintenance. Third, propose over-turn masking strategy that prevents penalization of over-turn responses (those that hit the maximum number of turns) during reinforcement learning, thereby balancing training-time efficiency with test-time scalability. De- spite training with an upper bound of only six interaction turns, our model gener- trajectories that naturally scale tens of turns inference time, with accuracy improving as the number of turns increases. Extensive experiments demonstrate that Mini-o3 produces rich reasoning patterns and deep thinking paths, effectively solving challenging visual search problems.
Accuracy on VisualProbe-
Correct:35.199
Avg @ 32
58
Mini-o3 (Ours)
DeepEyes
35. 1%
56
14
Mini-o3 w/o over-turn mask
Correct: 38.3%
52
DeepEyes
Mini-o3 w/o
50
48
over-turn masking
38.1% 1%
0.2%
46
44
42
40
38
Mini-o3
25.3%
16.3%
6.4%
36
1.1%
32
30
28
26
24
16 s turns <
24 turos < 32
8
24
Upper Limit of Turns During Testing
Figure 1: Left: Visual search accuracy continues to grow as the upper limit on the number of turns increases for Mini-o3. Right: Distribution of the correct trajectories under different numbers of interaction turns during testing. Mini-o3 demonstrates deeper thinking paths and stronger perfor- mance. Despite small upper limit (i.e., turns) during training, it shows the test-time turns scaling property: accuracy continues to grow as the maximum number of turns increases from 4 to 32.
"Equal Contribution
1 "ooresponning Author

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Original Image:
Question:
What is the text below 'CINKER PICTURES"?
Turn
The user wants to know what text is below text likely written sign banner. Looking the original image, there is a row of posters or signs the right-hand
"CINKER PICTURES
bounding box covering the area from approximately 0.5 to 1.0 horizontally and 0.3 to 0.7 vertically should capture the signs.
Observation
Observation
Observation 2:
unn
INEDE
Turn 10
Turn
Tur
The user wants to find the text below "CINKER PICTURES". there
Observation 4:
In Observation 3, see a poster with word "IMAGINE" on it, but it does not contain the text "CINKER PICTURES". need to go back a previous observation and look for another poster, In Observation 2, there is a poster to "IMAGINE" poster that didn't examine closely. appears have the text need.
"CINKER PICTURES' here. I must have zoomed into the wrong area. go back and look Observation 1 again. In Observation there are many little signs posters.
the
overlooked one. Let's go back Observation into different area...
Tur (Here, we omit Turn 5-9.)
Observation 10:
CAKEE RCCTULES SHANGHAI
Tum 11:
find text balow 'CINKER PICTURES". In Observation 10, finally see the text I was looking for. The sign clearly reads 'CINKER PICTURES*
and below that, says "SHANGHAI"..
<answer> SHANGHAI </answer>
Figure 2: A multi-turn trajectory generated by Mini-o3. It shows complicated reasoning patterns (e.g., trial-and-error exploration) and deep thinking paths (i.e., 11 turns) in visual search tasks. More illustrations given in Appendix.
INTRODUCTION
Recently, the capability to invoke image-centric tools has been incorporated into wide range of Vision-Language Models (VLMs). This thinking-wint wiitinggwwthiiaaaee capability enables flexible visual op- erations and ine-grained reasoning, substantially advancing visual understanding.
However, while existing open-source VLMs exhibit solid performance on relatively simple visual search benchmarks (e.g.. V" Bench (Wu & Xie 2024), HR-Bench (Wang al. (025)), they
2

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main weak on challenging tasks that require trial-and-eroor exploration. As shown in Fig Deep- Eyes(Zheng et al., 2025b) achieves only 35, 1% accuracy on VisualProbe-Hare We further observe that this underperformance limited interaction turns. For instance, in HR-Benche MR-BBech--4K, DeepEyes uses image tools for an average of merely turn per example. Unlike OpenAl o3 (OpenAl 2025), these models fail to produce diverse reasoning strategies (c.g., depth-first search, trial-and -error exploration, self-reflection) and deep thinking trajectories spanning tens of -ool-inteaactoon rounds.
difficult problems stems from monotonous reasoning patterns and
Motivated by these observations, we present Mini-o3 and provide a complete recipe to reproduce the thinking-wiin Brating-wwttimmit -mage capability with behaviors similar to OpenAI o3. As illustrated Fig Mini- o3 generates complex reasoning patterns and deep interaction trajectories, delivering unprecedented performance on challenging visual search tasks. Moreover, Fig (left) demonstrates Mini-o3's s abil- ity to scale the number of interaction turns at test time: accuracy consistently improves as the upper bound on interaction turns increases from 4 to 32 during inference, despite training with a budget of only 6 turns. By scaling both the depth of interaction and the diversity of reasoning patterns, Mini-o3 expands the solvable frontier of difficult problems, as shown in Fig. | (right).
Our training recipe comprises three components. First, we construct the Visual Probe Dataset, which contains thousands of high-resolution images paired with challenging visual search questions and answers. In contrast prior benchmarks (c.g., V Bench, HR-B HR-Bench), where targets often easy to localize, our problems are explicitly designed to require trial-and-error exploration. Notably, the inclusion of such challenging training samples is essential to elicit diverse reasoning patterns and deep interaction trajectories under reinforcement learning.
Second, we develop an effective pipeline to iteratively synthesize diverse multi-turn trajectories for cold-start supervised finetuning. Concretely, we begin by crafting a small set of representative demonstrations, each comprising the input image and question, along with per-turn observations, thoughts, and actions. These demonstrations cover varied reasoning strategies, including depth- first search, self-reflection, and goal maintenance. We then prompt an existing VLM to mimic these behaviors in few-shot manner and to produce the thought and action for each turn on new queries, iterating until the model completes the task or reaches the interaction budget. Only trajectories that culminate in a correct answer are retained. Importantly, the base VLM used for data synthesis need not possess native thinking- wiitiigg-wtt-timieee ability; in-context mimicking suffices.
Third, to enable scaling the number of interaction turns at inference time for harder problems, we avoid penalizing the over-turn trajectories (those that exceed the upper limit of interaction turns) and introduce an over-turn masking technique in reinforcement learning. Specifically, we mask advan- tages for trajectories that hit the upper limit of interaction turns or the context length. Consequently, over-turn trajectories are ignored during policy updates, and their losses do not contribute gradients. This simple yet effective strategy encourages the emergence of more complex reasoning patterns without overfitting short trajectories, thereby supporting test-time scaling of interaction depth. It also alleviates the need for a large training-time turn budget: in our experiments, we cap training at only 6 turns, significantly improving efficiency. For example, reducing the training budget from 16 to 6 turns shortens 10-day training run to about 3 days, with negligible impact on test accuracy.
2 RELATED WORK
2.1 VISION-LANGUAGE MODELS
The emergence of Vision-Language Models (VLMs) has marked a major milestone in artificial in- telligence by enabling the joint understanding of visual and textual modalities. Early seminal works, including BLIP-2 (Li et al. 2023a). Flamingo (Alayrac et al.) 2022), and the LLaVA series (Liu jt al. 2024 Li et al.) 2024a Guo et al. 2024), established a foundational paradigm that couples strong tranned vision encoders (e.g.. VIT (Dosoviiiskyy et al. (2200) with large language models (LLMs). These systems typically introduce a projector to align visual features with the linguistic embedding space, thereby endowing LLMs with visual grounding. Building on this paradigm, more recent multimodal models such as Gemini (Team et al. 2023). GPT-4o (Hurst et al.) 2024). and Qwen2.5-VL. (Bai et al. 2025), among others (Anthropic Meta Li et al. 2024b; Chen et al. 2024] Lin et al. 2024) have achieved state-of-the the-art performance on a wide range of visual understand- ing tasks, notably visual question answering. Their gains are largely driven by scaling model capac-
3

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Question Q
Overall Agentic Pipeline
What is the word before "STEFAN"?
Observation O
Input Question & Image
LOVE
CARIARE
Initial Query
Policy Model
Thought T,
The user wents me to find the word before 'TTFANN. In "obeervation_3 can see the text "LOVE BY CIRQUE DU SOLEIL'. However, cannot see the word "STEFAN" this cropped image. seems zoomed into the wrong part of the find need to back the original "STEFAN":
Thought T Action A;
Observation O
and try wider the 'orignaal image' a larger boundiing box capture more area, will a bounding box JO.S, 0.2. 0.9. 0.7] on the original Image to see If can find the word "STEFAN". </think-
Environment
Action A
<grounding
('bbox_2d 10.300009990000 'source': 'originalimmaee'
</groundings
Figure 3: The overview of our framework for multi-turn agentic image tool use. During each turn, the model generates the thought and action iteratively based on the previous observation (or the input question and image). The observation at each turn is obtained based on the parameters indicated by the corresponding action.
ity and training diverse, high-quality image-text corpora. In parallel, advances in reinforcement learning have enhanced the reasoning capabilities of VLMS by encouraging structured, step-by-step
problem solving via Chain-of-Thoughh prompting (Wei et al. 2022). Recent approaches (Meng)
et al. 2025 Huang et al.) 2025a Shao et al. 2024a, Liu et al. 2025b Zhang et al.] [2025a] Zhou
al. 2025) primarily target improved textual reasoning for challenging tasks, including counting.
logical inference, and mathematical problem solving.
2.2 TOOL-INTEGRATED AGENTS WITH REINFORCEMENT LEARNING
Progress in reinforcement learning (RL) including algorithms such as REINFORCE (Williams) 1992], PPO (Schulman et al., 2017), RLOO (Kool et al., 2019). ReMax (Li et al. 2023c). (025). Dr.GRPO et al., 2025a). and GSPO Zheng et al.) 2025a) has substantially reshaped training paradigms for both LLMs and VLMs. Systems like DeepSeek-RI (Guo et al. 2025) and Kimi-KI.5 Team et al. 2025b fur- ther demonstrated the efficacy of simple, verifiable reward signals in RL for improving reasoning quality. More recently, tool-augmented agents-such OpenAl's o3 and o4 (OpenAl 2025). Kimi-
GRPO Shao et al. (2024b). REINFORCE++
Researcher (AI 2025), Kimi-K2 (Team et al. (202a)), and others (Tao et al. 2025 Geng et al.)
2025 Li et al. 2025b Mai et al. 2025 Xue al.
shown strong agentic abilities in long-horizon multi-turn tasks by leveraging a broad toolkit (e.g., web browsing, code execution, retrieval). Complementary lines of work. including DeepEyes (Zheng et al. 2025b), Chain-of- Focus (Zhang et al., 2025b), and Pixel Reasoner (Su et al. 2025). as well as related methods (Zhu) af. 2025| Yang et al. 2025) Wu et al., 2025) Huang al. (0255), aim to equip VLMs with iter- ative zoom-in and region-of-ing interret selection, enabling active perception over images. While these directions collectively point to a promising path for next-generation visual understanding - partic- ularly challenging, compositional problems - current models often exhibit limited interaction depth and overly rigid reasoning patterns, constraining their effectiveness complex settings. Our work advances this line by presenting an effective training recipe for multimodal agent that sup- ports multi-tun turti-ttunmna image tool use, thereby improving adaptability and reasoning diversity in visually grounded tasks.
4

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Question What is written below 'OLUB6130'?
KM 747077
2G1.
226
OLU 8613 0
2G
7852 5004
Figure 4: Illustration of the Visual Probe dataset. The Visual Probe dataset features 1) small targets; 2) disturbance objects; 3) high-ress resoluteol images. As a result, is super challenging and requires iterative exploration and trial-and-error.
OUR APPROACH
3.1 OVERVIEW
Overall Agentic Pipeline We illustrate the overall agentic pipeline in Fig. 3 Given user query and an input image, the policy model iteratively produces a thought T; and an action A, The action interacts with the environment by invoking image tools, which yields
observation
O, This observation is appended to the interaction history and fed back to the policy model. The wetttt--acciooobbevttiiiii loop terminates when the model returns final answer or when prede- fined limits on context length or interaction turns are reached. The components are detailed below.
The internal reasoning process used by the policy model select the next action, conditioned on the interaction history and the current observation. We encourage diverse reason- ing patterss within thoughts facilitate trial-anderree exploration for challenging problems.
Thought T::
Action A₁: The action space comprises options: (1) grounding and (2) emitting final answer. For grounding, we parameterize the action with: bbox_2d: The normalized bounding box in 1J specifying the zoom-in region. source: The image which the grounding operates, chosen from 'original iimaee" or "observation_".. This design allows the model to act any prior observation in the trajectory.
Observation O₁: The observation produced by executing A, in the environment. Concretely, it is the image patch cropped either from the original image or from a historical observation.
Two-phase Training Our training procedure consists of phases.
Supervised Fine-Tuning (SFT): We first fine-tune the model on thousands of multi-turn trajecto- ries involving image tool use (i.e., cold-start data). The objective is to teach the model generate valid trajectories with diverse and robust reasoning patterns.
Reinforcement Learning with Verifiable Rewards (RLVR): We then apply GRPO (Shao et al. 2024b) to optimize the policy with verifiable. semantics-awer aware rewards. Because many ground- truth answers in our RL data require semantic rather than exact string matching, we employ an external I.IM as judge to compute reward signals. To maintain training efficiency and stability, we impose upper bounds of 6 interaction turns and 32K context length.
3.2 TRAINING DATA COLLECTION
Visual Probe Dataset Hard instances are essential for encouraging reflective, anal-and-error rea- soning during reinforcement learning. To this end, construct challenging visual search dataset,

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Multi-turn Examples (0, Tú, AD)
Query
Mimic
Thought T; Action A
VLM
Observation O
Execute Action
Figure 5: The pipeline of cold-start data collection.
the Visual Probe Dataset (VisualProbe). It comprises 4, 000 visual question-answer pairs for train- ing and 500 pairs for testing, spanning three difficulty levels: easy, medium, and hard. Compared with prior visual search benchmarks (c.g.. V" Bench), VisualProbe is characterized by: (1) small tar- gets, (2) numerous distractor objects, and (3) high-resolution images, as illustrated in Fig. 4 These properties make the tasks substantially more demanding and naturally require iterative exploration and trial-and-error.
Diverse Cold-start Data We initially attempted to train the model with reinforcement learning alone, without cold-start supervised fine-tuning (SFT). However, the model tended to produce con- cise responses and trajectories with few turns. We attribute this behavior to the base model's lack of exposure to long-horizon agentic trajectories during pretraining and instruction tuning (here, To handle complex exploratory tasks, we thus employ cold-start SET to activate multi-tur turn tool-use capabilities.
Owen2. Owen...5VII-- S-
The cold-start data collection pipeline is shown in Fig. 5 To generate high-quality, diverse multi- turn trajectories, we prompt an existing VLM with in-context learning ability using small set of manually crafted exemplars. The VLM is instructed to imitate the exemplars iteratively producing a thought and an action at each turn. The loop terminates upon emitting final answer or reaching pre-defined turn limit. We retain only trajectories whose final answers are correct. Following this procedure, we collect approximately 6, 000 cold-start trajectories from 6 exemplars.
3.3 REINFORCEMENT LEARNING
Lower Down Max Pixels The base model's context length is constrained to 32K tokens. With the default image budget of roughly 12M pixels, the allowable number of interaction turns becomes severely limited by context, which hampers trial-and-error exploration on difficult tasks. increase the feasible turn count per episode, we reduce the maximum pixels per image to 2M (or lower necessary). This simple adjustment allows more turns to fit within the same context budget, improving solve rates long-horizon problems.
Over-turn Masking In the vanilla GRPO setting, each question is passed to the policy model to generate a group of outputs for G Rewards are then computed based on the correctness context length limit, the reward is set 0, Subsequently, we compute advantages A by normalizing the rewards and update the policy using the GRPO optimization objective over mini-batches. In our implementation, we do not include KL or entropy regularization. Formally, the optimization objective is given by:
of the responses. Notably, when response hits the maximum number of turns or exceeds the
valid answer can be produced in such cases.
To(o: (q)
TT(0:
1 + A.) 41))
(o) BIg ~oTe (11))
A,, clip Thaid (0:1g)
T
ABald (o:lg)
(1)
A, F:
(2)
std( (ri, r2,..., rc))
However, observe that over-turn responses those that hit the maximum number of turns exceed the context length - are assigned zero reward, which translates into negative advantages after normalization. In effect, such responses are penalized and discouraged throughout training.

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Mini-o3 w/o over-turn masking
Mini-o3
53.3
Percentage (%)
40
29.522.
32.4
29.5
20
12.2
1.9
0 0.1
(1,
[4, 8)
[8. 16)
[16, 24)
[24, 32)
Figure 6: Distribution of interaction-turn percentages across five turn ranges during testing on VisualProbe-Harte The percentages calculated only on the correct responses.
01
F1
A1
A1
A2
0
0
q
03
r3
A
X
1
A3
0k
Tk
Ak
Turns Context Upper Limit
Rewards R
Advantages
Completion
Masked
Completed Response
Incomplete Response
A
Mask M
Advantages
Figure 7:
Illustration of the over-turn masking technique. The incomplete responses refer those that exceed the maximum limit of interaction turns or context length.
This design has two drawbacks. First, the correctness of over-turn responses is inherently unknown; blunt penalization thus injects label noise into the return signal and can destabilize training. Second, for efficiency, the turn limit during training must remain modest (typically fewer than 10 turns). As consequence, over-turn responses occur frequently In this regime, naïve penalization biases the model to answer prematurely, substantially suppressing the number of interaction turns (see Fig. 6). This makes highly challenging tasks intractable and severely constrains the potential of test-time scaling.
exceeding 20% at the beginning of training.
To prevent the model from collapsing into an "answer earlier" strategy, we propose an over-turn masking technique whose objective is to avoid penalizing over-turn responses. The overall procedure illustrated in Fig. 7 Concretely, addition to the rewards and advantages A defined as in vanilla GRPO, we introduce completion mask M that indicates whether response terminates successfully. We then compute masked advantages A M,- A,, so that over-turn trajectories (with M, 0) do not contribute negative learning signals. The modified objective, building on equation] T is summarized below, with the changes highlighted in red the formula.
JGRPO
Joper-ture
E Eigg (o.0), (110]
TT(0:)4)
A: Ai-Me, clip Te(oslu)
e, +e) A Ar
(3)
min
M
ABuia (0:1g)
To (oxlq)
M, f(oz| <= Coontext)
(tur((os) <= Cturn).
Here, |o:] and turn(o:) denote the token length and the number of turns in response o, respectively. Moreover, because some responses are incomplete, normalize the objective by the number of completed generations, Eg M,, rather than by the total number of generations G.

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Table I: Performance comparisons with existing models on visual search tasks. The sizes of all listed models are 7B. For VisualProbe and V Bench, we report Avg @ to reduce variance caused by randomness. We report Avg@8 and Avg@1 for HR-Bench and AME-Realworld, respectively.
VisualProbe
HR-Bench
Model
V*
MME-Real
hard
medium
easy
4K
8K
GPT-4o (Hurst et al.
2024)
11.2
15.4
47.5
65.2
62.0
58.3
45.2
LLaVA-Oner Vision (ii ct al. 2024a)
13,4
12.5
36.2
70.9
61.2
54.0
57.4
Owen2.5-VIL-Instruce Baa et 2025)
23.9
26.0
39.1
75.5
68.2
62.7
57.3
SEAL1 (Wu & Xie 2024
75.4
DyFo
Li ct al.
2025a)
81.2
Chain-of- Zhang et al. 2025b
88.0
Pixel Reasoner (Su et al. 2025
28.8
29.6
58.4
86.3
74.0
66.9
64.4
DeepEyest (Zheng al. 2025b
35.1
29.8
60.1
83.3
73.2
69.5
64.0
Mini-o3 (Ours)
48.0
50.4
67.0
88.2
77.5
73.3
65.5
The models only report the metric of Avg@I and the model weights not available. Re-evaluated using its official model and evaluation code to yield the metric of @32
With this technique, mask the loss for over-turn responses, thereby removing any implicit penalty. Notably, although we adopt a relatively small upper bound on the number of turns during training. test-time trajectories extend to dozens of rounds, with accuracy improving monotoni- cally. The proposed over-turn masking is thus essential for realizing the benefits of test-time scaling in the number of interaction turns, illustrated in Fig.
EXPERIMENT
4.1 EXPERIMENTAL. SETTING
Reinforcement Learning For reinforcement learning, follow DAPO (Yu al.) 2025 and adopt clip-higher, dynamic sampling, and token-level policy loss to ensure stable training. We set the group size 16. By default, the upper and lower clip ratios 0.30 and 0.20, respectively. The global batch size is 256, with mini-batch size of 32. We use constant learning rate of 1 x 10-6 Neither KL regularization nor entropy regularization is applied. To maintain training efficiency, we cap the maximum number of turns at and set the maximum context length to 32K tokens. We also implement asynchronous rollouts to accelerate training.
Dataset For training, we use the VisualProbe training split. In addition, to preserve performance on simpler visual search cases, we randomly sample 4, 0000 examples from DeepEyyes-Datasets 47k (Zheng et al. 2025b).. The test suites include VisualProbe-test, V Bench, HR-BBench, and MME- McalRcalworld (Zhang al. 20024)).
Evaluation Metric We find that single-run evaluation exhibits high variance and does not reliably reflect robustness due to sampling stochasticity. To mitigate this, we report the Avg@ metrie: each problem is evaluated K times with temperature set to 1.0, and accuracy is computed by averaging across the responses.
4.2 MAIN RESULT
The performance comparison between existing models and Mini-o3 on visual search tasks is pre- sented in Table 1 To ensure robust and convincing evaluation, we assess all models on VisualProbe, V Bench, and HR-Bench. Across all datasets, Mini-o3 achieves state-of-the-ar performance, sub- stantially outperforming other open-source baselines. We attribute these gains to Mini-o3's ability to sustain more complicated and deeper reasoning trajectories.
4.3 ABLATION STUDY
In this section, we present extensive ablation study to quantify the contribution of each component in our method. The overall results are summarized in Table 2 Unless otherwise specified, all experiments are conducted on the VisualProbe test set with the maximum pixel budget set to 1M.
8

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Table Ablation study for main components of the method. Max pixels are 1M. Upper limit
on the number of turns is set to 6 during training. Evaluations are made on VisualProobe test set.
ID
hard RL data
cold-start
over-turn
Hard
Medium
Easy
Avg. Turms (correct)
35.8
46.4
66.7
4.8
25.4
18.7
57.3
1.0
3
32.2
45.7
61.1
3.0
44.4
47.9
67.4
5.5
Table 3: Ablation study on the values of max pixels. Evaluations are made on VisualProbe test set.
Also, calculate the average number of interaction turns among overall and correct trajectories.
Max Pixels
Hard
Medium
Easy
Avg. Turns (All)
Avg. Turns (Correct)
0.5M
36.4
44.8
64.8
8.0
6.7
IM
44.4
47.9
67.4
6.3
5.5
2M
48.0
50.4
67.0
6.5
5.6
12M
36.1
40.7
62.1
1.0
1.0
Hard RL Data We compare experiments and 4 in Table Removing the hard RL data leads to performance decrease approximately 8.6 points on VisualProbe-Harte indicating that challenging RL samples crucial for encouraging complex reasoning trajectories.
Cold-start SFT To assess the necessity of cold-start SFT, we contrast experiments 2 and 4 in Ta- ble[] The results show that cold-start SFT is essential for multi-turn tool use: performance collapses without it. We hypothesize that the base model lacks exposure to multi-turn agentic trajectories dur- ing pre-training or instruction tuning, and cold-start SFT serves as a pivotal initialization.
Over-turn Masking A comparison between experiments 3 and in Table demonstrates that over-turn masking benefits reinforcement learning, particularly in multi-turn settings. It offers two main advantages. First, stabilizes training by avoiding incorrect penalization of truncated sponses whose correctness is inherently uncertain. Second, it enables test-time turn scaling and unlocks strong performance on highly challenging tasks that require substantially more turns than the training-time upper bound. This trend is further corroborated in Fig. 6
Max Pixels evalutees different maximum pixel budgets. We observe that both overly large and overly small settings are suboptimal. An excessively large budget induces premature "early stopping", reducing the number of interaction turns and limiting iterative refinement. Conversely, small budget increases perceptual hallucinations. We also report the average number interaction turns in the same table, which highlights a trade-off between perceptual accuracy and interaction depth. Optimal overall performance achieved by appropriately tuning the max-pixel budget.
CONCLUSION
In this work, we investigate multi-turn image-based tool use for Vision- Languag Models (VL.ss). To address challenging visual search problems that demand iterative exploration and trial-and-error, we introduce Mini-o3, a model capable producing diverse reasoning patterns and deep chains of thought. Its trajectories scale tens of turns, during which accuracy continues improve, yielding substantial gains over prior models on multiple visual search benchmarks. To enable these capabil- ities, develop three-pronged approach. First, we construct VisualProbe, challenging visual search dataset comprising both training and evaluation tasks. Second, we devise a simple yet effec- tive pipeline for collecting cold-start data by leveraging the in-context learning ability of an existing VLM. Third, we enhance vanilla GRPO with an over-turn masking strategy that prevents undue penalties responses that exceed the training budget on turns. This modification facilitates test- time turn scaling and enables the solution of particularly difficult problems. We believe this recipe offers practical guidance for reinforcement learning and the development of multimodal models with multi-turn interactions.

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ACKNOWLEDGEMENT
This work supported by the National Natural Science Foundation of China (No. 62422606, 62441615) and Hong Kong Research Grant Council General Research Fund (No. 17213925).
ETHICS STATEMENT
This work adheres high ethical standards in machine learning and computer vision, ensuring transparency, reproducibility, and fairness throughout all experiments.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, we provide comprehensive implementation details, including models, datasets, and training setups. All code, datasets, and models will be released.
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APPENDIX
A INFERENCE
Generation with Temperature During inference, we observe that greedy decoding tends to pro- duce repeated words or sentences, likely because the effective context grows with the number of turns. To mitigate this issue, simple yet effective method is to set the temperature to 1.0, which introduces sufficient randomness to reduce repetition without substantially degrading coherence.
B EXPERIMENTAL SETUP FOR SUPERVISED FINETUNING
During SFT, Qwen2. 5-VL-7B Instrrrct as the base model. Given the context-length constraints in multi-turn agentic interactions, we set the maximum pixel budget to 2M unless other- wise specified. We train approximately 6, 000 cold-start samples for 3 epochs. The learning rate set to x 10-5, and the global batch size 32.
C ABLATION ON UPPER LIMIT ON TURNS DURING TRAINING
Validation Accuracy
Upper limit 12
0.5
Upper limit 6
0.45
0.4
0.35
0.3
0.25
20
40
60
80
100
120
140
160
180
200
Training Steps
Figure 8: Accuracy VisualProbe-Hart during the training progress. The upper limit of the number of turns is set to 6 and 12, respectively.
To quantify the effect of a larger interaction-turn budget during training, we track the accuracy on VisualProbe-Hait Hard the course of training and compare budgets of 6 and 12 turns in Fig. 8 A lower budget leads to faster initial convergence, but the performance plateaus after approximately 150 steps. In contrast, a higher turn budget attains superior performance ceiling, albeit with slower convergence.
D PERFORMANCE ON GENERAL VQA BENCHMARKS
To ensure our model maintains strong performance general Visual Question Answering (VQA) tasks, we incorporate additional VQA data into both the cold-start and reinforcement learning train- ing stages. Our general VQA corpus is filtered primarily from open-source datasets, including LLLVA-OOeVision (Li et al., 2024a) and Cambrian-1 (Tong et al.) 2024). Note that during the cold- start phase, each VQA sample is paired with a reasoning trajectory generated by Gemini (Team) al. 2023
We evaluate our model on several general VQA benchmarks, including OCRBench (Liu et al. 2023) ChartQA (Masry et al.) 2022), DocVQA (Mathew et al., (022)) Math Lu et al. 2023 and POPE (i et al. 2023b). In addition, we further assess its performance on our VisualProbe- to validate the visual search ability of our model. The detailed results presented in Table 4 As shown, our model attains state-of tftte--f-th-aate performance on VisualProbe-tes while maintaining com- petitive accuracy across diverse general VQA benchmarks. These outcomes provide clear evidence
set,
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Table 4: Results on General VQA Benchmarks. We compare the performance of Qwen2.5-VL- 7B-Instruct and our model on various general VQA benchmarks, including reasoning, OCR-related tasks and hallucination. We also report their performance on VisualProbe-test. Our model achieves SOTA on VisualProbe-test while maintaining competitive performance on other tasks. Qwen2.5- VL" reports the results evaluated by Imms-eval (Zhang et al. 2024a).
Category
Benchmark
Qwen2.5- VL" (Bai et al 2025)
Ours
OCRBench
81.5
83.8
OCR-Related
ChartQA
79.6
77.4
DocVQA (val)
94.6
94.8
Reasoning
Vist (testmini)
68.2
68.8
Hallucination
POPE
86.7
90.8
VisualProbe (easy)
39.1
67.3
Visual Search
VisualProbc (medium)
26.0
50.5
VisualProbe (hard)
23.9
48.2
that incorporating general VQA data into the training process effectively preserves the model's per- formance on standard general VQA benchmarks.
E DETAILS ON LLM-AS-JUDOR FOR REWARDS.
We use external LLM judge model to evaluate the model's predictions against the ground truth. Notably, we only use pure text for judging during this process. The detailed prompt template shown below:
Table 5: Judgment Prompt Template. Question, Ground Truth and Prediction are dynamically replaced with the specific question, ground truth and model prediction during evaluation.
SYSTEM PROMPT:
You are an intelligent chatbot designed for evaluating the correctness generative outputs for question-answer pairs.
Your task is to compare the predicted answer with the correct answer and determine if they match meaningfully. Here's how you accomplish the task:
INSTRUCTIONS:
Focus the meaningful match between the predicted answer and the correct answer.
Consider synonyms or paraphrases as valid matches.
Evaluate the correctness of the prediction compared the answer.
USER PROMPT:
I will give you question related to image and the following text as inputs:
** Question Related to the Image** Question
2. ** **round Truth A Ground Truth
** **Model Predicted *n Prediction
Your task is to evaluate the model's predicted answer against the ground truth answer, based on the context provided by the question related to the image. Consider the following criteria for evaluation:
* ** Does the predicted answer directly address the question posed, considering the information provided by the given question?
** **Accurccy*** Compare the predicted answer to the ground truth answer. You need to evaluate from the following two perspectives:
(1) If the ground truth answer is open-ended, consider whether the prediction accurately reflects the information given the ground truth without introducing factual inaccuracies.
it does, the prediction should be considered correct.
(2) If the ground truth answer is a definitive answer, strictly compare the model's ' prediction to the actual answer. Pay attention to unit conversions such as length and angle, etc. As long as the results are consistent, the model's 's prediction should be deemed correct. **Output # *.
Your response should include an integer score indicating the correctness of the prediction: for correct and 0 for incorrect. Note that I means the model's prediction strictly aligns with the ground truth, while 0 means it does not.
The format should be Score: 0 or
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When evaluating the reward score, perform keyword matching the evaluation of model's response. Specifically, we first match the pattern "Score: " and then parse the subsequent number, which is either 0 or 1, to obtain the final reward score.
F DETAILS ON COLD-START TRAJECTORY GENERATION.
In this section, we present the specific prompt template and corresponding in-context examples that were used in the cold-start data generation phase. We first provide our prompt template as follows:
Table 6: Agent Actions and Generation Format.
You are an agent that answers the questions by using following two actions:
grounding(xx, yl, x2, y2], source): Return the bounding box [w1, h1, w2, h2] for the region on the 'source' observation image, where (w1, h1) and (w2, h2) are the top-left and bottom-right coordinates (note: and represent the width and height; the width and height of the image are normalized to 1.0; the coordinates of top-left corner are (0, 0).), and 'source' could be either "originalllmaac" or "observation_i" meaning one of the history observations;
2. final return the final answer.
Your generation should have the following format: Thought: to analyze the question & observation and inform the action. Action: your action
Here are several examples:
Table 7: Multi-turn Trajectory Example Format.
Example N:
Question: Question
Original Image: Original Image
Thought i: Thought
Action 1: Action
Observation i: Observation
Table 8: Instructions for Thought Generation.
Thought i:
Action i:
Please follow the language style of the above examples to finish the thought and action. You should mimic human to think carefully and comprehensively. Output the thought detailed as possible, along with some useful description of the observation, suspective conjectures, and underlying world knowledge that might be useful for problem solving. If the target object is present in observation i, please analyze whether final answer is ready or further grounding is needed.
Otherwise, you can go back to either original image any of the history observations to find another region. In this case, clarify this thinking process, and analyze how the old bounding box deviates from the correct one, and how you obtain the coordinates of the new bounding box in Thought
The output format should be in JSON as:
"thought": "xxx", "action": "grounding ( ([x1, yl, x2, y2], xxxrree="xxx)
Below show the visualization results of the in-context examples used.
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Original Image:
Question:
is written in English on top first line the right side of wooden wall in the middle the picture?
Thought
The user's question asks about the text on the right side the wooden wall. To analyze it, may need to zoom into the center part of the image. It seems like the text is too small, so I'll have to crop that region and possibly enlarge the image to read properly. It could be something like "meeting room" or "conference room, but I'll need to visually inspect and confirm.
see there might be small metal letters on the wooden wall, likely on the right side. To check the text, I'll crop the image around the dimensions of 0.45-0.65 0.3-0.6. After cropping, I'll zoom in to make it easier to read. This should help me see the text clearly, allowing me to provide the most accurate answer based on this zoomed-in section of the image.
Action
grounding (0- 45.0.3...655.00000 source="original )
Turn1 Observation 1
Thought 1:
see small metal letters on the right side of the central wooden wall. To focus on just the text, I'll crop the image more narrowly, focusing only on the text area. By narrowing the crop horizontally, can zoom in specifically on the text, which should help me read it more clearly. This should allow to zoom in and get more detailed and accurate result. The target text located approximately at the top half of this image, let's focus the range from 0.25 to 0.5 of the height dimension in the image observation
Action 1:
source="observatoo__1"
Thought 2:
noticed there might be small metal letters the right side of the central wooden wall, which facing the camera. But could only the first line of English text partially observation 2. should retum to observation and try a better bounding and resulting observation seems high, should downwards from (0.05, 0.25) (0.05, 0.35), and
Tum2 Observation 2
The previous bounding box (0.05,0.25 2.2.0........ 0.95.0.50) the top left
of the bounding bottom right corner from (0.95, 0.50) to 0.95, 0.55). Therefore, the new bounding box should be 10.05,0.35 115,0.35,0.955 0.9,00.55) to better see the text. Once cropped, can display it and zoom to get a clearer view of the text. This should help me identify the text in greater detail!
Action 2:
Thought 3:
Turn3 Observation 3
Upon dloser inspection, can clearly the English text the top first line is "Meeting Room". The final answer confirmed. further checking needed.
Action M"eeting Room")
Figure 9: In-context Example 1.
18

---

Original Image:
Question:
What are the black numbers on the yellow license plate?
Thought
The user is asking about black numbers yellow license plate image. Let me locate that plate. It looks like it's a small yellow reclangle on scooter or cart, on the center-left the image behind a walking man, focus on area and zoom in get clearer look. display the cropped region, along with grid for better accuracy, we can make sure we've found the correct part.
The plate seems to be in the central region, around the left-center, behind man on a scooter or tricycle. try cropping that central area for closer look. I'll crop from coordinates around 0.4-0.6 horizontally and 0.4-0.7 vertically. It's bit tricky, but I'Il zoom in and annotate the cropped section before showing it, so can confirm that I'm targeting the right part of the image.
Action
grounding(( http:/diiin(044.0444000000000000 source=foriginal "imienaiaiiiiiaa_mmmaa"
Turn1 Observation
Thought 1:
can't click on the image, so inspect by cropping an approximate area. It looks like scooter tricycle, and it's toward the left-center. focus cropping that central region (around 0 0.4-0.6 x 2.4-0.7) and zoom in further to hone in on the license plate, identifying it as a Thai plate with blue and yellow colors. I'll adjust the cropping slightly lower to refine the area. The license plate approximately at the bottom-left region, around 0.0-0.4 horizontally and 0.6-0.8 vertically. I'll zoom on this area.
the behind
Action 1: grounding((0.0000..... source=
Thought
The user wants to know what black numbers on yellow license plate are. clearly. might be part longer number, but surrounding characters, possibly Thai, are obscuring the rest. The license plate likely belongs to tuk-tuk, as these often have four digits. Thai characters like "nm" seem to represent Bangkok. I'll crop further right to check if other digits are visible past the "6" that's currently uncovered. The previous bounding |00.0.0.600.40033 and we need to extend the bounding box further right. The bottom-right corner of the bounding box is 8..4, so we should set the width from 0.4 a larger number, possibly 0.8. Accordingly, the new bounding box should 10.000.6.0.8.0..8..
Turn2 Observation
MB
AN
Action 2:
grounding (0..000000000000..00..03 source=
Thought
The user asked about the black numbers the yellow license plate. can see that the only visible number is "6." There seems to be other Thai characters ("nw") beside it, and possibly more digits to the right, but they're concealed the in the image. The only visible black number "6." couble-checken and don't alteraae digits numbers.
Turn3 Observation
The license plate shows "6" on the right side, possibly followed by more digits, though obstructed. On the left side, there's a gap where the Thai characters "nvi" might be visible. Given these observations, it's likely there's to the plate, but it's being occluded by "6" for answering the question, as it's the only clear number at this moment. The only black digit that's visible on that yellow license plate is a "6." Any other numbers that might on the plate are blocked by the person standing front it
focus the visible
Action
final("6*)
Figure 10: In-context Example 2.
19

---

Question:
How many people are there total under the two yellow facilities beneath the building on the right?
Original Image:
Thought 0:
This is object recognition and counting problem. need to count the number of people under the two yellow facilities located beneath the building the right side of the image.
First, located the building on the right side of the image. builliing that looks like church, with tall tower. Beneath this building, found two yellow facilities, which look like temporary enclosures construction area. guess the region (0.6-1.5 6-1.0 width and 0.2-0.8 height) should include two yell ow facilities. Let's zoom in to see more clearly.
Action
groounding(0.60022100000 source="original_mm
Tum1 Observation
Thought
noticed two yellow facilities beneath the church-ike building. Several people are seemingly sitting there and they might be chatting. The user asked how many people there were. There might be three or four people total, but couldn't be certain as the region blurry in this image. To count more accurately, need to zoom in on the area where these two yellow facilities are located, based on observation 1. They appear in the central region of observation 1, approximately 0.3-0.6 horizontally and 0.5-0.8 vertically.
Action 1:
grounding((03305550000000 source= somm::0
Turn2 Observation
Thought
this further zoomed-in image, could see the two yellow facilities more clearly, on the porcelain floor. Under each facility, there might be two people ttakking Maybe four people in total are seerringyy over there. But couldn't be certain as this region looks blurry to now. may need doser look based on obs observation to verify. The target area is located at the central this image, so I'll crop this area (0.4-0.8 horizontally, 0.4-0.8 vertically) and see more details.
Action
grounding (0. 0,0.444.008880..... source="observattin
Turn3 Observation
Thought
is dlaar now. Two people talking under yellow facility and another two people are under the other. verifies my previous judgement is correct. Now, can confirm that there are total 4 people under the yellow facilities.
Action final("4")
Figure 11: In-context Example
20

---

Question:
Whel is the number below the string 207784?
Original Image:
Thought 0:
Idenity this number recagnition rquiring
datarmining the diglts "207784" may be license plate
Based on my experience,
number. I Lobeerve an altandoned truck with ia front facing the
camera, located in the ventral part of the image. Maybe "207784"
the Iconse plate number of this truck. Illl cmp the front part
this truck to chock whether the above hypethesis
front part
the image,
approsimsttlly h
horicontally and 0,45-0 verically.
Action 0:
grounding(0 4,000. sourcer Image")
Tum1 Observation 1
Thought 1:
The front part of the track can be soon in this image. However, there bounding focus
iconsa number here.
includiing the left one the right truck lights present confimm that
region?
analyoe
indeed
the froni of the truck. There would be license plate, bul might be abandoned for a long time and the loonse plate was lost. Up to now, we confimm that there are no digits "207784" around the abandoned truck. Next. locale the target string
should comtinue to search for other
2277784"
original image, approximataly 0.55-0. wkth and height. Lets zoom
lop-right
aroa based on the original image.
Action
grounding( 00t0:ggg0......0.888888008 source ooggnaa Image
Tum2 Observation 2
Thought
nolic
while characlers in crop. and they are
"EMCUS6445199 and "22G1" The user asks for the number below
"207784" Noither matches the target digh string. So nead to continue
searching the
number.
abserve white characters
number being sought. Now, the original image,
which might
lop-tell part (0 0-0 25 horizontally, 0. 0.0-0.5 5.5 veriisall) the ginal image.
Action
Tum3 Observation 3
groundngt00000000 25,00. "orgial="igina_iimmeeee
Thought 3:
in this crop. obeerve many while numbers on the left side, which might include the digils being sought My largel is to find the dighs that match "207784", Let's observation Based observation 0.1-04 height. crop
the top left
region appreximatelly 0.0-0.3 the bounding box [0.0,0. 1.0.3.444
Action 3:
(rounding00..0001110 0.000.0....33300.... "ibeee"cbeervalinn...
Thought 4:
Turn Observation 4
notice that there are two lines of rumbers the boltom left. They look little bil biurry, probably "85093922" and "22G1" I not cortain that they are exactly what but
that neither them matches the target
locale the target digits. "4" at top of observation 4, which could be pari of the number "207784" asked for in the taok need to look lifll upwards to see the entire digii sequence. Let's retur observation and move the previous bounding box little upwards. The previous bounding was 1,0 What about mowing
other
Hilled
10% 10..0.0..003.03333
Action 4:
(rounddgg00.....00300000 "obsevatiooiniiiionn..
Turn
Observation
Thought
seem a blurty user esked the number below "207784". would be highly Ikaly to be what we are seaking for. The number halow looks like "6" need to double-ched further zooming on this ama. It looks like the upper part of abservation 5, mppelsmatell 0.3-0.7 horizonaally and 0.0-0.6 verticaly. So let's this
crop.
pari.
Action
mrouddiggo.330000023
source source""deervvainnn obeervation.
Tum6 Observation
Thought 5:
A verical line mads "SU 207784 in this crop, which allgns with my pravious Jdgoment, though as little can confim that number "207794" number directly below
Action 6: fnail"))
Figure 12: In-context Example 4.
21

---

G STATEMENT OF LLM USAGE
In this work, LLM is used fix grammar enrors and polish language expression. But it is not used in technical ideation.
H MORE ILLUSTRATIONS OF MULTI-TURN TRAJECTORIES
In this section, we present more examples of the trajectories produced by our model as follows.
Original Image:
Question:
What the directin the arrow below text "PRAKING" point?
Turn1:
The user asking for the direction of
located below the text "PRAKING". ... can the road, below traffic light. ... is likely that this sign has the text "PRAKING" on The sign located approximately the region defined by the coordinates [0.7, 0.4, 1.0, 0.7]. This area seems to contain the sign and the arrow in question.
sign on the right
Turn2
Observation 2
Turn4
Observation
Turn
Observation
CHASE
Turn4:
The user is asking for the direction of the below the text "PRAKING". Observation
zoomed-in of the signs on the right side of the intersection. corner of this image, can see another street sign. It's a bit small, but appears to have the word "PARKING". ... need to zoom in even closer on this specific sign to see the direction of the arow clearly. The sign is in the bottom right corner of Observation 4, within the region defined approximately by the coordinates 10.7. 0.7, 1.0, 1.0).
Turn5
bottom right
Observation 5
Tum5:
The useris asking for the direction of the arrow below the text "PRAKING" Observation 5, have successfully located the relevant sign. The sign is clearly visible reads "PARKING" with an arrow pointing to the right. Therefore, have found the information needed answer the question. The direction the arrow is to the right.
<answer> Right </answer>
Figure 13: example of visual search in urban intersection generated by Mini-o3. The scene shows busy city street with multiple signs and traffic elements. Our model conducts multi-turn reasoning progressive zoom-in, hypothesis revision, and backtracking to identify the direction the arrow pointing.
22

---

Original Image:
Question:
What is the number below "67.200"?
Observation 1:
Turn1:
The user is asking to find the number below the text "67 "67.200". The text appears to be one of the containers. will zoom into the area of the image where the containers are find more dearly.
Turn
Observation 2:
Turn2
Observation 3:
Observation 4:
D1T1
Turn4:
IC DIESR
MAX GRISS
Turn4
realize can see the text
MAX. GROSS TARE
"67.200" observation 3.
Turn3
The number directly below
is "22G1" which is visible. will go back to lobservation 2.
CUBGO
62,310
(1 DAP
1110
Turn6
Observation 5
Turn5:
The user wants to find the number below the text '67.200" on the container. Looking at observation 5, can dearly that the text am looking for "67 200" under the line "MAX. GROSS Below "67 200", the number clearly visible as "22G1". can definitively so
FCIU
352849 ZI
22G1
nns.
confidently provide the final answer. </think>
MAX GRISS TAEE
18480
<answer> 22G1 </answer>
2323
An example of visual search in a container yard generated by Mini-o3. The scene de- picts stacked shipping containers with dense text markings. Our model performs multi-turn reason- ing targete zoom-ins, cross-checking across observations, and corrective backtracking the string "67.200 "67.200" and read the number directly beneath it. It outputs the correct value. "22G1", demonstrating precise text localization and robust step-by-step verification cluttered setting.
Figure 14:
locate
23

---

Question:
What are the numbers on the circular road sign above the triangular road sign?
Observation 7:
Or Original Image:
Turn7
Observation 11:
Observation 1:
Turn1
Observation 12:
Turn17
Observation 17:
Tumn12
Tum17:
now clearly road sign this image. the middle-right area. appears be warning sign. To get a dlearer look, will zoom in specifically on that area.
Observation 18:
Tum188
Turn 18:
The user wants to find the numbers on the circular road sign. In "observation 18'iioon__________ I've zoomed on an area containing a road sign. A triangular sign is visible right below the circular one. The numbers on the circular sign are clearly visible, so can confidently say the answer is "30".
<answer> 30 </answer>
Figure 15: An example of visual search in lakeside village generated by Mini-o3. Our model performs multi-turn massning--corre-eee vations- to localize circular road sign above triangular warning sign. Mini-o3 ultimately recog- nizes the digits "30" on the sign after 18 reasoning turns.
zooming, refocusing, and verification across obser-
I MORE ILLUSTRATIONS OF FAILURE PATTERNS.
In this section, we present examples of two primary failure cases: Grounding Error and Inability Backtrack.
24

---

Original Image:
Question:
What color the hat wom by the child in the yellow shirt?
Ground Truth: White.
Ground Truth Region
Turn1:
The user wants to find the color of the hat wom a child yellow shirt. First, need locate the with people in the image to find the child weaning ng yellow. The main street with people seems to be the center of the imrgge, will crop this investigate
Turn1
Observation 1
Tum2 Observation
Tum2:
identify child wearing a yellow shirt siting the wall. The child appears be wearing a pink or light-oolore hat. To confirm this and determine the exact color with more clarity will sitting on the wall in the current
observation
the group chi dren observation. ww..
Tum8
Observation 8
TumB:
Grounding Error:
in 'observation 8 and see they wearing blue hat. can confidently say the color of the blue. can see enough detai make a final decision, so provide <answer> Blue < </answer>
can clearly identify
The image shows child a white shirt, not a yel low shirt. The model has made a wrong conclusion!
answer.
Figure 16: Failure case: Grounding Error.
Original Image:
Question:
What color are the shorts on the person in a black vest?
Ground Truth: White.
Ground Truth Region
Tum1:
The user is asking about the color of the shorts of First, will locate spot
person wearing black
a black vest in the image. person in a black vest near the right side the mage in the middle ground, walking or running. To better see their appearance, crop this area.
Tur1
Observation
Tum2 Observation
Tum3 Observation 3
Tum4 Observation
Inability to
The user wants to identfyy the color of the shorts of the person weading the black vest. have clear view in observation 4.1 can now dearly make out the shorts, and they appear be blue with some white vertical and horizontal stripes, which suggests patterned light blue, possibly denim.
The model gets stuck single region, neglecting to verify it's correct.
<answer>> Light <lanswer>
Figure 17: Failure case: Inability Backtrack.
25


Reviews:
[{"review_id": "FO4UtpKJEo", "reviewer": "Reviewer_SzFD", "summary": "Mini‑o3 is a multi‑turn visual search system that scales reasoning to tens of steps by combining a new Visual Probe dataset, an iterative cold‑start trajectory pipeline, and an over‑turn masking RL strategy that avoids penalizing responses hitting the turn limit. Trained with only six turns, it generalizes to longer test‑time trajectories where accuracy rises as the allowed turns increase, achieving state‑of‑the‑art results on visual search benchmarks.", "questions": "- Interesting that ChartQA performance dropped after the RL training compared to Qwen2.5-VL. I am curious about what the authors think might be the reason? Is it because of the data domain used for RL training?\n- Curious how much improvements on general perception benchmarks, like BLINK, and CVBench. and also spatial reasoning benchmarks, like SAT.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Mini-o3 achieves state-of-the-art performance on several visual search benchmarks, clearly outperforming previous open models. The improvement is from allowing the model to perform more reasoning turns. The over-turn masking method improves long-turn reasoning while keeping training efficient.\n- The new Visual Probe dataset introduces difficult visual search tasks. Mini-o3 trained on Visual Probe has demonstrated some sort of trial-and-error reasoning.", "weaknesses": "- Missing details\n    - The paper relies on an LLM judge for rewards, however the exact judging prompts/criteria are not fully specified in the main text.\n    - The dataset difficulty splits (easy/medium/hard) are introduced and used in results but their criteria are only briefly mentioned. I would suggest the author to add more details on how the data split are decided.\n    - Similarly, the cold‑start trajectory generation details (for example, exact prompts, hand-crafted in-context examples, acceptance filters) should be included in the main text or appendix.\n- I wonder whether the authors have considered some other variants for over-turn masking\n    - Mask only context-overflow instead of turn-overflow\n    - Soft penalty instead of zero-mask for over-turn content\n- Missing ablation of removing the DeepEyes portion or changing the ratio between VisualProbe and DeepEyes from the training to see how much the mixed-data balance matters.", "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": "flnCF5orUN", "reviewer": "Reviewer_ku2Y", "summary": "This paper presents **Mini-o3**, an open-source vision-language agent designed to perform **deep multi-turn reasoning** for visual search tasks. While prior open-source VLMs (e.g., DeepEyes, Chain-of-Focus) often exhibit shallow, repetitive reasoning with limited tool-use turns, Mini-o3 demonstrates reasoning trajectories spanning tens of steps. The authors propose a three-component recipe:\n\n1. **VisualProbe dataset**—a new benchmark of high-resolution, hard visual-search problems requiring trial-and-error exploration;\n2. **Iterative cold-start data synthesis**—an in-context prompting method for generating diverse reasoning trajectories for supervised fine-tuning;\n3. **Over-turn masking**—a reinforcement-learning modification that avoids penalizing long, incomplete trajectories, enabling scalability beyond the training turn limit.\n\nTrained with a 6-turn cap, Mini-o3 generalizes to much longer inference trajectories (up to 32 turns), achieving **state-of-the-art accuracy** on VisualProbe, V* Bench, HR-Bench, and MME-Realworld benchmarks. The model shows more diverse reasoning strategies and deeper “thinking-with-images” patterns than prior open-source agents.", "questions": "Please see weaknesses above.\n\n1. How sensitive is over-turn masking to the masking threshold or turn budget?\n2. Do long-turn trajectories ever degenerate into loops or repetitive zooming?\n3. How well would Mini-o3 scale with a larger base model?\n\nMinor Grammar & Typo:\n\nLine 014 - 015: “We address this limitation by scaling up tool-based interactions and introduce Mini-o3…” should be \"introducing”\n\nLine 018: “OpenAI o3–style behaviors” should be a regular hyphen", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* Addresses a **clear and under-explored gap**: enabling deep visual reasoning in open-source VLMs.\n* **Elegant and reproducible training recipe** with quantitative and qualitative validation.\n* **Comprehensive experiments** (four benchmarks + ablations).\n* Demonstrates **turn-scaling property**—a rare capability among existing agents.\n* Good ethical and reproducibility practice; code/data promised for release.", "weaknesses": "- [Reward Modeling] The paper employs an external LLM as a semantic judge for reinforcement learning but provides few details about its consistency, inter-run variance, or bias. It is unclear how reward noise affects policy stability or whether calibration against human-verified scores was attempted.\n- [Ablation] While ablations exist, they focus on quantitative accuracy. The paper could include deeper analyses of emergent reasoning behaviors—e.g., trajectory diversity, failure patterns, or qualitative diagnostics showing how reasoning depth relates to success rates.", "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": "UUiIUVanNJ", "reviewer": "Reviewer_iPEo", "summary": "The authors propose Mini-o3, a method capable of performing deep, multi-turn reasoning and achieving state-of-the-art performance on challenging visual search tasks.\nThey constructed a multi-turn visual reasoning trajectory dataset and introduced an over-turn masking strategy to balance training-time efficiency with test-time scalability.", "questions": "Have you tried testing the performance of different sizes of QWEN-VL-2.5?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The main innovation lies in the proposal of an over-turn strategy. In addition, it demonstrate that although only up to 6-round data  are used during training, the accuracy consistently improves as the upper bound on interaction turns increases from 4 to 32 during inference.", "weaknesses": "The setting of the 6-round budget has not been proven to be optimal.", "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": "ABf5lQQ4Rt", "reviewer": "Reviewer_XKVc", "summary": "This paper introduces Mini-03, a vision–language model designed for deep, multi-turn visual reasoning in complex visual search tasks. The authors propose a three-part training recipe: \n\n- VisualProbe Dataset – A challenging benchmark with high-resolution images, small targets, and distractors to necessitate iterative reasoning. \n\n- Cold-start Data Pipeline – An iterative synthesis process generating ~6,000 multi-turn trajectories from a few exemplars for supervised fine-tuning. \n\n- Over-turn Masking – A reinforcement learning modification to GRPO that prevents penalization of trajectories exceeding the turn limit, enabling test-time scaling. \n\nBuilt on Qwen2.5-VL-7B-Instruct, Mini-03 demonstrates emergent test-time turn scaling: trained with 6 turns, accuracy improves up to 32 turns at inference. It achieves state-of-the-art performance on VisualProbe-Hard (48.0% vs. 35.1% for DeepEyes) and competitive results on V*Bench and HR-Bench. Ablations confirm the necessity of cold-start SFT and over-turn masking.", "questions": "- How sensitive is performance to the choice and diversity of cold-start exemplars? \n\n- Which LLM was used as the reward judge, and how was its reliability validated? \n\n- Can you provide theoretical or empirical analysis of why over-turn masking enables test-time scaling? \n\n- Does the approach generalize to other reasoning domains beyond visual search? \n\n- What are the failure modes—e.g., repetitive loops, grounding errors, or inability to backtrack?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- A reproducible recipe combining dataset, data generation pipeline, and RL. \n\n- Over-turn masking is simple yet effective, unlocking test-time scaling—a property with implications beyond vision tasks. \n\n- Promising results across benchmarks, systematic ablations, and insightful analysis of resolution vs. interaction depth trade-offs. \n\n- VisualProbe fills a gap for evaluating exploratory reasoning.", "weaknesses": "- The paper does not explain why over-turn masking enables scaling—mode collapse prevention or exploration diversity? \n\n- Relies on a teacher VLM for trajectory synthesis; sensitivity to exemplar quality is unexplored. \n\n- Reward computation details (model choice, prompt, bias analysis) are missing -- reproducibility concerns. \n\n- Focused on visual search; generalization to other reasoning domains (math, scientific figures) is untested – narrow scope. \n\n- Systematic study of error patterns or robustness to adversarial perturbations would strengthen the paper.", "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)