AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning
Authors: Mingyang Song
Haoyu Sun
Jiawei Gu
Linjie Li
Ranjay Krishna
Yu Cheng
Venue: ICLR 2026 Poster (2026)
Keywords: MLLMs
Visual Tools
Reinforcement Learning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: AdaReasoner enables multimodal models to dynamically orchestrate tools for complex reasoning, achieving state-of-the-art performance and emergent self-adaptive tool use.

Abstract: While augmenting Multimodal Large Language Models (MLLMs) with tools is a promising direction, current approaches face critical limitations. They often rely on single, atomic tools, failing to address the challenges of multi-turn planning, and they do not equip models with the ability to select effective tool combinations for complex tasks. To overcome these limitations, we introduce AdaReasoner, a framework that teaches models to perform dynamic tool orchestration for iterative visual reasoning. Our paradigm is designed to support a broad spectrum of tools, including computationally intensive, expert-model-based services.
It features a comprehensive design that includes a new data curation methodology and a tailored Tool GRPO algorithm to optimize multi-turn tool-calling trajectories, which yields state-of-the-art models that achieve substantial gains over their baselines (+38.7\% average on 7B) and reach near-perfect accuracy on complex benchmarks like Visual Spatial Planning (97.6\%). This performance surpasses leading proprietary systems such as GPT-5 and Claude Sonnet 4, demonstrating that our approach can effectively overcome scale-based limitations by augmenting smaller models with powerful tool-use capabilities. Critically, we find that AdaReasoner develops emergent, self-adaptive behaviors: it learns to autonomously adopt beneficial tools, discard irrelevant ones, and modulate its usage frequency. This ability to curate its own optimal problem-solving strategies represents a significant step toward building more robust, scalable, and reliable reasoning agents.

Published as a conference paper at ICLR 2026
ADAREASONER: DYNAMIC TOOL ORCHESTRATION FOR ITERATIVE VISUAL REASONING
Mingyang Song*.., Haoyu Sun":?, Jiawei Gu Linjie Li RRajjay Krishna Yu Cheng".
l Fudan University, "Tongji University, "National University of Singapore,
Uniieesitty of Washington, 'The Chinese University of Hong Kong
Homepage: https: http:::/addreassonee.iiiLauo
Code: http:///waa.aaaooooc////aaaaaaaa
com Models and Data: http://pp///agggggaaooeo/////aaa
ABSTRACT
While augmenting Multimodal Large Language Models (MILMs) with tools is a promising direction, current approaches face critical limitations. They often rely single, atomic tools, failing to address the challenges of multi-turn planning, and they do not equip models with the ability to select effective tool combinations introduce AdaReasoner,
for complex tasks. To overcome these limitations, framework that teaches models to perform dynamic tool orchestration for itera- tive visual reasoning. Our paradigm is designed support broad spectrum of tools, including computationally intensive, expert-model-based services. It fea- tures a comprehensive design that includes a new data curation methodology and a tailored Tool GRPO algorithm to optimize multi-tur turn tool-calling trajectories, which yields state-of-the fittheeart art models that achieve substantial gains over their base- lines (+38.7% 7% average on 7B) and reach near-perfect accuracy on challenging benchmarks like VSP (97.6%). This performance rivals even surpasses leading proprietary models such as GPT-5 and Claude Sonnet 4, demonstrating that our approach can effectively overcome scale-based limitations by augmenting smaller models with powerful tool-use capabilities. Critically, we find that AdaReasoner develops emergent, self-adaptive behaviors: it learns to autonomously adopt bene- ficial tools, discard irrelevant ones, and modulate its usage frequency. This ability to curate its own optimal problem-solving strategies represents a significant step toward building more robust, scalable, and reliable reasoning agents.
INTRODUCTION
Multimodal LLMs have made steady progress on vision language tasks, but a core challenge in multimodal reasoning remains. The problem lies in two areas: fine-graines perception and multi- step reasoning. On tasks such as visual spotting (Shu et al.) 2025 Zhang et al. 2025a), models can often locate a relevant region but fail capture the key details inside it. Without this evidence, their language skills become ungrounded and default to semantic priors, leading to "guided guessing": outputs that sound plausible but are brittle and detached from the image. The weakness not in language generation itself, but in perception - the lack of iterative probing and refinement of visual understanding. Addressing this requires a shift from passive recognition toward structured reasoning and active manipulation of visual elements (Qi et al. 2024 Li et al. 2025a).
A promising direction for addressing this limitation is dynamic multimodal interaction (Lin al. 2025), where the model iteratively refines visual states and reduces hallucinations. This aligns with the Extended Mind Theory (Clark & Chalmers] 1998), which views external tools as integral to cognition. For visual reasoning, tools should not be static add-ons, but active supports for manipu- lating and refining visual representations. Early SFT- and prompt-based methods (Ma et al., 2024] Hu et al. 2024) explored the use of multiple pre-defined tools, but typically relied on scripted invo- cation rather than active planning. More recent RL-based efforts, such as DeepEyes (Zheng et al.
"Equal contribution.
'Fqual Advisory Contribution

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Published as a conference paper at ICLR 2026
Visual Spatial Planning
Hmm... Which tool helps me think better this time?
Point
AStar
Draw
GUI-QA
Tool Invocation Frequencies
T
XXX
Buy Now
XXXX
Discord
OCR
Crop
OCR
RL Training Precess
AdaReasoner adaptively selects the necessary tools to solve complex reasoning tasks. The model demonstrates the ability to acquire new tools, discard ineffective ones, and dynamically adjust the frequency of tool usage during both training and inference time within different tasks.
Figure 1:
2025) and Pixel-Reasoner (Su et al., 2025b), enhanced perception through cropping-basee search, yet restricted themselves to single-tool trajectories. Across both lines of work, what remains missing is the ability to plan, adaptively select, and coordinate tools roolssencgnizing that deciding which tools use, when use them, and how combine them is itself a critical form of multimodal reasoning.
We present AdaReasoner, tool-aware reasoning agent that addresses the limitations of prior single-tool scripted tool-use approaches. To bootstrap this learning, introduce new data curation pipeline that generates complex, multi-turn trajectories, explicitly modeling reflection and backtracking. This initial policy then refined using our adaptive reinforcement learning paradigm, which is tailored to optimize these multi-turn, tool-planning strategies. We have also curated high-quality dataset of multi-turn tool-use trajectories that incorporates a variety of sophisticated operations. AdaReasoner learns adaptively plan and combine tools in multi-turn settings through cold-start and RL, following an iterative process of observing, manipulating, verifying, and reflect- ing. Our toolset supports both manipulation tools (e.g., DRAWLINE, INSERTIMAGE) and perception tools (e.g.. POINT, OCR). It spans lightweight offline utilities as well as advanced model- -based ser- vices. As illustrated in Figure [ this design allows AdaReasoner to not only extract and check visual evidence but also actively transform it, yielding deeper multimodal reasoning.
Through adaptive tool interaction, AdaReasoner achieves substantial and stable gains across diverse benchmarks, with the 7B model improving by +38.7% % on average and reaching near-pen cear-perfect ac- curacy on tasks such Visual Spatial Planning (97.6% % 31.6% baseline). It also surpasses proprietary systems, outperforming Claude Sonnet 4 on VSP (97.6% vs. 56.3%) and GPT-5 on Jigsaw (96.6% % vs. 80.1%). Beyond accuracy, AdaReasoner demonstrates how tools shape reason- ing: perception tools help models to see, manipulation tools help models to verify, and planning tools help models calculate. Crucially, as shown in Figure AdaReasoner exhibits self-adaptive tool-use behaviors. It learns to select effective tools, discard irrelevant ones, and regulate their use according to task demands and feedback, revealing strong flexibility and generalization. This ad- dresses the long-standing question of which tools should be included and how models should learn to use them, suggesting that with proper training, MLLMs can autonomously curate tool-use strate- gies from a broad candidate set and extend their visual reasoning capacity in goal-directed manner. In summary, our main contributions are as follows:
We propose a comprehensive method for developing tool-augmented models, built upon three core innovations: data curation method for multi-turn tool planning, adaptive RL framework for multi-turn tool interaction, and a versatile tool suite supporting both lightweight tools and heyy services.
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Published as a conference paper at ICLR 2026
Based on method, we introduce AdaReasoner, for complex tool planning, which develops emergent, self-adaptive behaviors, learning to au- tonomously adopt beneficial tools, discard irrelevant ones, and modulate its usage frequency.
family of state-of-the-an models
Our AdaReasoner achieves significant gains over their base counterparts and delivers performance that is competitive with, superior to, leading proprictary models like GPT-5 and Claude Sonnet on arructued-reasoonigg tasks. This establishes that our methodology can elevate smaller, open- source models to the taae-offthee-ae
2
RELATED WORK
2.1
REINFORCEMENT LEARNING FOR MULTIMODAL REASONING
The recent success of DeepSeek-RR RI (Guo et al. 2025), which demonstrated that rule-based Group Relative Policy Optimization (GRPO) can effectively induce strong reasoning behaviors in LLMs, has spurred wave research aimed at replicating this paradigm in the multimodal domain. Several studies have successfully extended this approach, with Zhou et al. (2025) reproducing the emergent "aha" moment in MLLM reasoning, RI-One Vision (Yang et al. (025a) introducing a cross- mrosll formalization pipeline, and works like Feng al. (025) and Li al.
(2025b) improving temporal
reasoning in videos. A collection of other strong works have also leveraged R R1-style methods to achieve impressive results in general MILM reasoning (Huang al. 2025 Shen al. [025 Lu
itt al. 2025). However, a key limitation of the RI-style, rule-based reward structure is that it primar- ily targets the reasoning process and does not directly improve the model's underlying perceptual abilities. Since accurate perception is the foundation for sound reasoning, error accumulation from faulty perception can still lead to hallucinations and degrade performance. AdaReasoner directly addresses this shortcoming. By leveraging the precise perceptual capabilities of external expert models and specialized tools, our framework ensures a high-fidelity understanding of the visual input, thereby improving the reliability of the entire reasoning pipeline.
2.2 TOOL-AUGMENTED MULTIMODAL REASONING
There is a growing interest in enhancing MLLMs with sophisticated tool-use capabilities. Early efforts focused on foundational aspects such as infrastructure and data. LLaVA-Plus (Liu et al.) 2024a), for example, introduced a dedicated tool server to provide services for MI.LMs. On the data front, CogCoM (Qi et al. (0244) identified six key manipulation strategies and trained models syn- thetic Chain-of- ShaainoffMaanipllttte Manipulation (CoM) data, while TACO (Liu et al.) 2024b) contributed a large-scale dataset of reasoning traces derived from 15 visual tools. Subsequent research has explored different paradigms for tool interaction. One prominent line work enhances visual reasoning by training models to generate code (Zhang et al., 2025b Zhao et al.). While powerful, these code-based envi- ronments are ill-suited for integrating computationally intensive capabilities, such as invoking large expert models. Another line of research leverages simpler, atomic visual tools like zoom-in func- tions to augment model perception (Wang et al., 2025 [Zheng et al.) 2025 Su et al. 2025a Zhuu et al.) 20256 Su al., 2025c). However, these approaches typically focus on single-step actions and have not explored the more complex challenges of multi-turn planning or dynamic tool composition. Our work, AdaReasoner, designed to bridge these gaps, providing framework that enables models to perform multi-turn planning and reasoning while adaptively selecting from diverse suite of tools.
3 METHOD
3.1 PRELIMINARY
Problem Formulation As shown in Figure 2 we formalize tool-augmented multimodal reasoning as a sequential decision-making process. An MLLM represented as a policy ho parameterized by weights 0, is tasked with solving problem by generating reasoning trajectory т. The policy is equipped with access to a predefined set of visual tools T {ti,..., }.
A trajectory is sequence of state-actionnoossevvaii tuples that represent the model's step-by-step reasoning process:
{(S0, do on), (81,01, 01), -... (ST, aT, or))
3

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Published as a conference paper at ICLR 2026
(b) Tool Server
<thlnk>
<hink-
<think- My presions paths incorrect...
<think- re-plan path (222)
To
Iiseed to call Point locate the
continue locating points
Compute Henvy
Light
Data
<ithink>
weight
Abstract Traj
Thk
Call
Obs
Retlection
Retection
Ans
Point
V
AStar
OCR
Insertimg
Tool Call Data
tz Crop
Legruup
(1)
(1.)
Reward Rules
Update
Rivel
Thk
Call
Obs
Ans
Reward R
Recouracy
Call
Obs
Ans
Riteraat
Policy Model
Reward R.
Rionaa (n)
Rionnat (Tx) Reormar
(c) Muiti-Turn Tool
An overview of our AdaReasoner framework. The process consists of two stages: (a) a Cold Start phase, where the trajectory specially designed for multi-tun reasoning, and (c) Tool GRPO phase, where the policy is refined via reinforcement learning guided by adaptive, multi-turn reward. The central Tool Server (b) manages diverse suite both lightweight and compute-heavy tools, enabling all interactions throughout the pipeline.
Figure 2:
Here, 8; denotes the problem state, a, E is tool-calling action encapsulated by special tokens, and is the resulting observation from the tool's execution. Each action de induces transition from state 8, to 8 based on the new information in o,:
90
8T+1
(2)
Visual Tools Our AdaReasoner framework is built upon diverse and powerful suite of visual tools, which it executes and integrates directly into the reasoning process. This toolset inten- tionally designed to cover three reasoning functions: perception (e.g., POINT, OCR), ma- nipulation (e.g., DRAWLINE, INSERTIMAGE), and calculation (e.g., ASTAR). Furthermore, this suite seamlessly integrates both lightweight, offline tools for immediate execution and computation- ally intensive, expert-modee--ased online tools. These foundational capabilities are summarized in Table I with detailed specifications for each tool provided in Appendix B.I
3.2 HIGH-QUALIH TRAJECTORY DATA CURATION
As illustrated in Figure 2h data curation follows unified, three-stage process designed to generate high-fidelity, human-like reasoning trajectories.
Abstract Trajectory Design First, for each task, manually design abstract, optimal problem- solving blueprint. For example, the VSP trajectory follows a perception-plaminn planning- verification logic, Jigsaw mimics iterative trial-and-error process, and GUIQA involves a focus-then-extract strat- egy. However, to ensure the model develops true robustness beyond simply following these "perfect" paths, deliberately incorporate critical types of complex scenarios:
Reflection and Backtracking: We include trajectories designed to encourage a process of trial and verification. These feature explicit self-correction steps where the model must reflect on sub-optimal outcome and backtrack, teaching it to actively validate its own hypotheses and learn from intermediate failures.
To prevent over-reliance on external tools, we introduce cases where tools fail or return erroneous results. In these scenarios, after recognizing that tool is not providing a useful output, the trajectory prompts the model to fall back on its own intrinsic capabilities to generate "best-effort" answer, ensuring develops resilient, dual-strategy approach.
Explicit Tool Failure:

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Published as a conference paper at ICLR 2026
Table Visual tools integrated within AdaReasoner. We illustrate their arguments, outputs, and core functions description. More detailed descriptions of our tools are presented in Appendix B.
Tool
Description
Arguments
Tool Output
POINT
Point to a target objoct
Image Description
Point coordinates
DRAW2DPATH
Draw a path using directional commands
Image Stat Directions
Image with line
ASTAR"
A to find the shortest obstacle-free path
Start Goal Obstacle
Shortest path
ORIECTRLACKARRR
Detect pure black areas in an image
Image
Bounding boxes of black arens
INSERTIMAGE
imuge
al bounding box position
Image Coordinates
Combined imuge
OCR
Extracts and localizes text from the image
Image
Text with their bounding box
Crop region augment
Image
Coordinates
Cropped Image
Tool Calling Supplements Subsequently, we ground these abstract blueprints by programmati- cally executing the tool calls to populate them with concrete, real-worke inputs and outputs.
CoTT Data Generation Finally, we leverage a powerful LLM to generate the corresponding Chain- of- of-Thhught (CoT) reasoning that connects each step. This process yields a final dataset of rich, tool- augmented trajectories that teach the model not just what tools to call, but why and how to reason between them. Details for our trajectory data curation can be found in Appendix B.2
3.3 MULTI-TURN TOOL GRPO
To train our model for complex multi-turn tool-planning scenarios, extend the GRPO framework to effectively handle multi-turn tool-calling reasoning trajectories. Concretely, we use Multi-turn Reward Accumulation and Adaptive Tool Reward to ensure the efficacy of the RL procedure.
Multi-turn Reward Accumulation Our total reward, Recaa is formulated as Reotal = Riforma (Arool
Rool Agce Ra with each component adapted for multi-turn trajectories T {
R (To)ma((T)) Correct formatting is mandatory at every step. Therefore, the overall format reward for trajectory is within it is correctly formatted. A single format error at any turn results in Riomat O, nullifying the entire reward for the trajectory. This enforces strict adherence the reasoning structure.
Format Reward Riormat II==1
if and only if every individual step
Tool Reward The overall tool reward is the average of the fine-grained scores from all tool- calling turns (from To to -T) It is calculated as Rual + Di-0 (T.). Each individual tool call, Reool (Te), is evaluated using a hierarchical score of 0-4 based on four criteria (Structure, Name, Parameter Name, and Parameter Content).
Accuracy Reward This reward is granted only based on the final turn, Tr. If the final answer is correct, Race = 1; otherwise, it is 0. Asce and Aooo are hyperparameters, the effects of which are analyzed in detail in Section
Adaptive Reward for Encouraging Tool Use. To guide the model to use tools as a reliable aid when uncertain, introduce adaptive reward mechanism with an asymmetric incentive struc- ture, where the reward calculation is contingent on the final answer's correctness. Correct trajec- tories automatically receive the maximum possible reward points), irrespective of whether tools were used, thereby rewarding efficient solutions (including forgoing tools when unnecessary). Con- versely, for incorrect trajectories, the reward calculated component-wise. This creates powerful safety net that trajectories with proper tool use can still earn partial credit (up to points), while those that forgo tools and guess incorrectly are heavily penalized with zero reward. This design teaches the model that while direct answers are optimal when confident, a structured, tool-assisted process is the superior strategy when facing uncertainty. (See Appendix B.4 for details).
EXPERIMENTS
4.1
EXPERIMENT SETTINGS
Models Our core experiments are conducted on the 7B-Instruce models (Bai al. 2025). These models selected our primary testbeds due their strong open-son open-source performance in visual understanding, allowing us to effectively demonstrate the impact and scalability of our proposed methods across different model sizes.
and Qwen2.5-Vi V--
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Published as a conference paper at ICLR 2026
Table 2: Our main results on VSPO, VSP, Jigsaw, BLINK-J, GUIChat, and WebMMU benchmarks. TC, TG means Tool Cold Start and Tool GRPO, respectively. The best performance is highlighted in bold, while the second-best performance is indicated with an underline.
Model
VSPO
VSP
Jigsaw
BLINK-J
GUIChat
WebMMU
Nav
Verify
Overall
Nav
Verify
Overall
Avg.
Act.
Compre.
Rea.
Qwen 2.5 VL 328
7.56
53.12
28.36
24.33
45.40
33.91
59.50
64.67
85.21
71.27
85.98
68.65
61.82
Qwen 2.5 VL 72B
17.22
52.34
33.41
28.00
52.40
39.09
70.10
71.33
88.01
77.10
91.06
74.59
14
InterVLL3 788
7.22
52.60
28.14
21.67
51.20
35.09
52.80
60.00
79.83
62.47
71.34
73:27
51.25
GPT
26.89
42.86
34.25
48.17
64.60
55.64
80.10
73.33
71.41
62.13
80.49
68.65
45.96
Gemini 2.5 flush
15.44
68.96
40.12
34,50
76.40
53.55
67.20
65.33
83.05
69.31
66.26
73.93
69.46
Claude sonnet
37.56
67.92
51.36
48.17
66.00
56.27
58.60
65.33
93.14
71.61
83.54
77.23
60.50
Qwen2.5 VL 3B
5.67
50.91
26.53
7,50
49.80
26. 26.73
39.
48.67
45.11
45.39
$5.89
51.82
34,95
SFT
27.42
42.60
55.51
61.38
32.31
Direct GRPO
2.78
50.00
24.55
18,33
50.00)
32.73
42,70
52.67
$2.49
48.44
$6.30
51.49
41.41
TC
14.67
51.09
44.72
24.82
Our TG
11.22
50.00
29,
22.67
50.00
35.09
433000)
47.33
89. 60
58.88
72.15
62.05
47,87
TC
73.00
84.73
92.17
97.80
94.73
63,48
81.71
53.01
+67.33
+-47.53
+58..5 20
+844.67
+48.00
+68.00
+55 .0
++40.00
+-0.334
+18.09
+25.82
+5.61
+18.06
Qwen2.5 VL
9.84
52,60
$9.46
62.67
77.03
69.64
49.19
Direct SFT
33.68
51.30
42.18
42.67
$1.40
46.64
86.40
88.00
62.68
55.62
65.65
63.70
44.79
GRPO
10.33
12,50
51.40
30.18
67.67
70.19
83.54
60.94
Our TC
31.58
94.01
61.69
41.00
93.60
64.91
84,20
83,33
61.85
51.63
64.63
54.13
41.12
TG
52.47
59.70
55,20
73.18
72.30
80.67
92.52
72.97
88.62
64.61
Our TC + TO
73:44
98.70
85.09
96.33
99.20
97.64
96.60
96.00
88.57
68.16
82.32
67.33
58.30
+63.60
+47.85
+82.17
+66.00
+29.11
+5.29
+9.11
Baselines We benchmark approach against comprehensive set of baselines. (1) SOTA Pro-
prietary Models: GPT-55202550007 (OpenAl 2025). Claude-sommet--422255005
(Anthropic 2025),
and Gemini-2.5-fassh (Comanici et al., 2025) (2) Competitive Open-Source MI.LMs: Qwen-2.5-
VL-32/72BBInstrruc (Bai et al. 2025) and InternVL-3-788 (Zhu et al. 2025a). (3) Direct SFT: We
take base models supervisedly finetuned on the training set of each task as strong baseline (Yang)
et al. 20256). (4) Direct GRPO: Following prior work (Zhou et al., 2025), we apply rule-based
GRPO to the base models to enhance their reasoning ability, serving as another strong baseline.
Tasks Our approach is evaluated across three diverse tasks designed to probe distinct facets of multimodal reasoning: Visual Spatial Planning, for multi-step planning and perception, evaluated on our custom out-of-distributio benchmark (VSPO) and the standard VSP benchmark (Wu et al.) 2.24]. Jigsaw, for visual compositionality, evaluated on our Dissa--OCC dataset and the Jigsaw subset from BLINK (Fu et al.) 2024) and GUIQA, for fine-grained GUI understanding, evaluated on GUIChat (Chen et al. 2024) and WebQA from the WebMMU benchmark (Awal et al. 2025). Detailed settings and implementation details for all tasks are provided in Appendix
4.2 BRIDGING PERCEPTION AND REASONING VIA VISUAL TOOLS
AdaReasoner could bring stable improvements As shown in Table 2 our AdaRcasoner framework con-
330
94.7%
sistently and dramatically improves the performance of
base models, demonstrating an average gain of +38.66 %
Accuracy (96)
on the 7B model. This tool-augmented approach trans- forms tasks like VSP from a low baseline (~31.64%)
Clmnee Somnet 58.3%
near-perfect execution (97.64%). This performance significantly surpasses traditional optimization methods such as task-specific SFT (46.64%) %) and Direct GRPO (30.18%) Furthermore, AdaReasoner enables our 7B model to achieve state-of-the-an results that are compet- itive with, and in structured-reasoning domains, superior to the best proprietary models. For instance, on VSP and Jigsaw, our model outperforms Claude Sonnet 4 (97.64% % vs. 56.27%) ) and GPT-5 (96.60% vs. 80.10%) respec- tively. This confirms that AdaReasoner is highly effec- tive strategy for unlocking advanced reasoning capabili- ties in open-source models.
Aug. Boundary
Cludee Sornet-4
Glenn 25 owen W.
Alotcesorer AlsReasoner 76
Overcoming scale-based lim- itations with tool augmentation. On the VSP task, our tools boost the perfor- mance of both 3B and 7B models, ele- vating them from disparate baselines to a near- unirouniform high performance.
Figure 3:

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Published as a conference paper at ICLR 2026
AdaReasoner could help overcome scale-based limitations Furthermore, results reveal that tool augmentation can redefine the performance ceiling of MLLMs by overcoming scale-based limitations. As illustrated in Figure [ while the baseline performance of 3B and 7B models is dis- parate and low, our tool-augmented versions both achieve near-perfect accuracy (94.7% and 97.6%) This strongly indicates that the primary performance bottleneck has shifted from the model's 's intrin- sic scale to the extrinsic quality of the tools it wields. Consequently, this establishes a powerful paradigm where even smaller, more efficient models can achieve state-of-the-ar results, contingent not on their size, but on the instruments they are equipped with.
4.3 TOOLS HELP MLLMS TO SEE, VERIFY AND PLAN
Our framework decomposes complex reasoning tasks into manageable steps, each resolved either by the model itself or by high-precision external tool. This design fundamentally shifts the problem- solving burden: instead of requiring flawless internal reasoning, the model's primary task becomes effective tool planning. By delegating precise sub-tasks to reliable tools, the model is freed to focus on its core competencies of judgment, synthesis, and integrating the resulting outputs.
Perception Tools Help MILMs to See Our framework leverages expert perception tools to overcome the intrinsic perceptual limita- tions of MILMs. As shown in Table 3 and
Comparison of start-point localization accuracy between Molmo-7B-D (Deitke al. 2024) and the Qwen-VL series base models.
Table 3:
4 in VSP-verification, our expert POINT tool
Model
Accuracy
achieves perfect localization accuracy (100.0% vs. "50.0% for baselines), and providing its co- ordinate output as context boosts the down- stream zero-shot reasoning performance by an average of +18.79 points. This principle holds even with imperfect tools: for the Jigsaw task, our BETECTBLACKARG. tool provides robust 72.6% % accuracy, offering a significant perceptual advantage that underscores the value of delegating these challenges to specialized tools.
Qwen 2.5 VL 3B Instruct
2.47
Qwen 2.5 VL 7B Instruct
47,01
Qwen 2.5 VI. 32B Instruct
6.54
Qwen 2.5 72B Instruct
50.0
Our POINT Tool (Deitke et al. 2024
100.0
Manipulating Tools help MILMs to verify Our manipulating tools empower the model to formulate and subsequently verify its own hy- potheses. For example, in the VSP-Verify task, we teach the model to call DRAW2DPATH to explicitly draw red line on the frozen lake question picture. The problem is thus converted to verifying whether the red line crosses the blue ice holes. As shown in Table 4 even un- der zero-shot context-appending setting, the DRAWLINE does help improve the judge accuracy of the model, yielding an average performance improvement of +7.82 points. Similarly, the Jigsaw task, the model can actively invoke the IN- seRTIMAGE tool to compose and evaluate different candidate pieces by inserting them into the original image, enabling more informed decision makiii....
Table 4: Impact of tool-augmented context on zero-shot reasoning accuracy for VSP VSr-Verify task.
Ver-iyr
Model
Base
tw Line
/w Point
Qwen VI.3B
50.91
57.92 (+7.01)
(-1.22)
Qwen 2.5 VL
50.85
57.68 (+6833
57.87 (47.02)
Qwen 2.5 VL 32B
53.12
61.31 (+8.19)
87.87 (434.75)
Qwen V1.
52.34
61.57 (+9.23)
87.53 (435.19)
High-Quality Trajectory data help MLLMs to plan While augmenting context with tool out- puts is effective for zero-shot reasoning, this strategy alone is insufficient for achieving optimal performance. Tool-Cold-Stan addresses this gap by explicitly teaching the model two foundational capabilities: how to use tools correctly and how to recognize the patterns where they should be ap- plied. As shown in Table for the 7B models, adding the Tool-Cold-San yields a massive performance improvement of +24.93 points on VSP and +19.82 points on Jigsaw compared to using Tool-GRPO alone. Besides this, the inclusion of reflection data during the Cold- Start phase provides further benefits the model's 's reasoning. As shown in Table when A search is disabled, training with reflection data yields a substantial improvement over the no-reflection checkpoints (91.3 36 vs. 67.27).
phase before Tool-GRPO
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Table 5: Adaptability study on the VSP and VSPO tasks. Stage compares our full Tool Cold Start (TC) + Tool GRPO (TG) pipeline against TC alone. Reflection indicates training with (v) ) or without (X) reflection data, A# specifies tool availability: during Reinforcement Learning (RL), at Inference (Inf), or unavailable (-). A* Statistics report calls per sample and success rate.
VSP
VSPO
A# Statistics
Stage
Reflection
A
Nav
Verify
Overall
Nav
Verify
Overall
CPS
Suce Rale
TC TG
X
RL
96.33
99.20
97.64
73.44
98.70
85.09
0.56
100.00
TC+
84.33
99.80
91.36
63.89
99.61
80.36
0.00
0.00
TC TG
Inf 55.17
84.60
68.55
57.22
99.61
76.77
0.68
16.89
TC TG
X
Inf
62.33
80,00
70.36
43.78
88.70
64.49
0.52
94.53
TC Only
X
41.00
93.60
64.91
31.58
94.01
61.69
0.00
0.00
TC TG
X
44.83
94.20
67.27
27.67
94.81
58.62
0.00
0.00
TC Only
X
Inf
46.00
79.40
61.18
32.11
81.43
54.85
0.49
85.16
4.4 MLLMS CAN LEARN ADAPTIVE TOOL-USING
To investigate whether MILMs can effectively learn to select tools and adaptively regulate their usage frequency, we carried out a systematic study to build an adaptive tool planning model, which is the main characteristic of our AdaReasoner.
MILMS Can Use New Tools during Inference Time During inference, the model demonstrates remarkable ability to generalize, dynamically adapting its tool-use strategy to solve novel problems. To probe this capability, we investigated whether the model could leverage a powerful new tool, ASTAR, that was intentionally withheld during the Cold Start phase. As shown in Table [5] when the A# tool is introduced solely at inference time (Inf), it provides significant performance boost to the relevant task. For our standard CS+GRPO model (without reflection), this elevates the VSP navigation score from 44.83 (without A*) to 62.33. The A* Statistics corroborate this adaptive behavior, showing a high invocation success rate of 94.53%, which indicates the model is not just guessing but correctly learning the tool's syntax and purpose in a zero-shot setting. However, this adaptability also reveals critical challenge. The presence of the new A tool, which is irrelevant for the verification task, acts as a distractor and degrades performance. For the same model, the Verify score drops from 94.20 80.00 when the tool is made available. Notably, models trained with Reflection data appear to develop more rigid policy, as they fail to effectively incorporate the significant performance decrease in navigation.
new tool, leading
Learning to Adopt and Master Beneficial Tools through RL To increase the stability of the adaptive tool calling, utilize RL for teaching the model not just use a new tool, but master its application context. We start from the same SFT checkpoint that has never been exposed to the A* tool. We then introduce A as an available option during our tool GRPO procedure. The results, shown in Figure 4 and Table 5 are compelling. The key findings are as follows.
Learning to Adopt Beneficial Tools As illustrated in Figure 4a for the Path Navigation task (warm-colored curves), the model's invocation frequency for ASTAR progressively increases, sta- bilizing at high rate of over 1.0 call per sample. This upward trajectory indicates that the model, guided by task-completion rewards, correctly identifies ASTAR as a highly beneficial tool for pathfinding and actively incorporates it into its problem-solving strategy. As result, this mastery translates to a dramatic performance increase, with the VSP navigation score soaring to 96.33, which achieves the best performance.
Learning to Discard Irrelevant Tools Second, and just as critically, the model learns to discard the tool when it is irrelevant. Figure (a (cool-colored curves) shows the inverse trend for the Veri- fication task. The model initially explores using the A tool but, receiving no reward for doing so, gradually learns to suppress its usage, with the invocation frequency decaying towards zero. This adaptive pruning prevents the negative interference observed in the zero-shot inference scenario, allowing the Verification performance to remain at near-perfect 99.20. Similar phenomena can be found in the GUIQA task as well, where the crop tool is discarded by the model under pure Tool-GRPO settings. This demonstrates that the model learns to suppress the usage of irrelevant tools, avoiding the potential interference or inefficiency they might introduce.
Learning to Modulate Tool-Use Frequency Beyond the binary choice of adopting or discarding tool, the model exhibits more nuanced capability: dynamically modulating the invocation
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frequency continuously useful tools to find an optimal balance. This is evident in the usage patterss of Point and DRAW2DPATH (Figures 4b and 4c). For instance, in Figure 44b the model learns that the Point tool is significantly more critical for navigation, maintaining a high and stable call frequency (~ 3.2 calls/sample), while keeping its usage minimal for verification (~ 1.0 call/sample). Similarly, for DRAW2DPATH, the model converges towards a moderate and stable invocation frequency for both tasks after an initial period of exploration. This behavior suggests that the tool-aware GRPO procedure enables the model to self-correct and fine-tune its tool-use strategy, converging on task-specific, efficient invocation patterns.
ABLATION STUDY
We systematically adjust Apol and Aace to evaluate their influence on learning dynamics and final performance. Specifically, we train the model on the same VSP task data for 100 RL steps un- der different reward-weight settings, monitor the training curves to ensure convergence, and then evaluate each checkpoint's 's performance. The results are summarized in Table
As shown in Table 6 the model's performance consistently improves as the ratio Aust : Aace increases. This indicates that larger tool re- wards not only accelerate convergence during RL training but also lead to significantly better final performance. These results validate that our rool-reward design is effective and plays crucial role in helping the model learn tool call- ing more efficiently and robustly.
Table 6: Ablation reward weight configura- tions for VSP and VSPO.
Auai Ave
VSP (S)
VSPO (%)
Nav
Verify
Overall
Nav
Verify
Overall
51.83
71.45
41.78
75.58
57.37
1:2
49.50
95.80
70.55
36.44
94.29
63.11
78.73
96.23
90.33
96,80
93.27
70.33
96.36
82,34
QUALITATIVE RESULTS
Our qualitative analysis is shown in Figure 5 which reveals that AdaReusoner-7B's superior per- formance stems from its robust, process-oriented methodology, in contrast to the brittle, monolithic approach of baselines like GPT-5. This methodology manifests in three key capabilities. First, it per- forms multi-turn tool planning and reasoning, breaking down complex tasks like VSP-Navigation into logical sequence of perception, planning, and verification. Second, it exhibits reflection, as seen in the Jigsaw task, where it actively evaluates imperfect tool outputs ("neither insertion per- fectly resolves...") and adjusts its strategy from simple trial-and-erros to more nuanced comparative judgment. Finally, it demonstrates synergistic tool use; in GUIQA, it strategically combines Crop and OCR, using the former to create an ideal, unambiguous input for the latter, enabling focused and accurate information extraction.
7 CONCLUSION
We introduced AdaReasoner, a training framework combining Tool-SFT and RL that teaches models not just to tools, but compose them in dynamic, task-aware manner. Our experiments show

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Published as a conference paper at ICLR 2026
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Our AdaReasoner-7B 7B demonstrates advanced capabilities for multi-turn, tool-assisted reasoning and reflection, enabling it to achieve performance that is on par with, or even superior to, state-of-the-ar closed-soure models.
Figure 5:
that this approach leads to state-of-the-aas performance and, more importantly, endows models with adaptive capabilities: they learn to adopt, discard, and modulate tool use as needed. Our central finding is that by effectively leveraging tools, the primary barrier to performance shifts from the model's 's inherent scale to the tool's external accuracy. This paradigm enables smaller models to achieve performance previously attainable only by larger models.
ETHICS STATEMENT
This research adheres to the ICLR Code of Ethics. Our work aims enhance the reasoning capa- bilities of MLLMs through tool use, and we are committed to scientific transparency by detailing our methodology and planning to release our training and evaluation framework to encourage repro- ducibility. We acknowledge the potential for dual-use, more capable agents could be misused. The outputs of AdaReasoner may reflect biases from its base models and the tools it uses. All data used is either procedurally generated from public benchmarks, with no private user data collected. We believe the benefits of enabling smaller models to perform complex reasoning and advancing the understanding of agentic AI are significant, and we encourage continued research into the safety and alignment of such systems.
REPRODUCIBILITY STATEMENT
Detailed descriptions of our methodology, covering both the Tool Cold Start and Tool GRPO stages, provided in Appendix B In addition, comprehensive experimental settings, including all hyper- parameters, are documented in Section 4. and further elaborated in Appendix C.3] To ensure full reproducibility and allow others to build upon work, we will publicly release complete source code code- covering the entire framework for data curation, training, and evaluation -along with all custom-generated data upon publication.
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A USE OF LARGE LANGUAGE MODELS
In adherence to the ICLR policy on the use Large Language Models (LLMs), we explicitly state that LLMs were not used generate any of the core ideas, methodologies, experimental results presented in this paper. The conceptualization of the AdaReasoner framework, the design of the training pipeline, and the analysis of the results were conducted entirely by the human authors. We did, however, utilize LLM-based tools (such as Google's Gemini) for tasks related to improving the manuscript's 's language, clarity, and grammatical correctness, akin to using a spelling or grammar checker. All final text was reviewed and edited by the authors to ensure it accurately reflects our original contributions and findings.
B
METHOD DETAILS
B.1
BASIC SETTINGS
We first formalize multimodal reasoning with tools as agentic planning process, enabling sys- tematic description of how models decompose and solve complex tasks.
Problem Formulation We denote MLLM with tool-using capability policy Ty. param- eterized by model weights 0. At initialization, Ty is equipped with access to a pool of tools T = 11,12; 12..... Lu- where denotes the number of available tools. Given a task description and the original multimodal input æ text, image, the system begins from an initial state sp- Build- ing on this setup, the planning framework formalized through three essential components. State represents the current problem status: the initial state Sy corresponds to the original input, while intermediate states capture textual reasoning steps conditioned on accumulated observations, until special token triggers an action. Action denotes a one-step tool invocation, delimited by the spe- cial symbols <tool... caclllll and </tool. tttoolccall 1>, which executes a selected tool. Observation or is the execution result returned by the invoked tool and is incorporated into the subsequent state.
typical tool-integrated reasoning trajectory involves multiple tool invocations over several rea- soning steps, which can be represented sequence of rounds:
7 = {oo, T1, T2, ,2...., }r}
where each round is defined T {si, ai, 0i and the sequence proceeds follows:
8o
$1
8T+1
To enable the model to autonomously generate reasoning traces and tool calls, utilize system prompt as shown C22 during rollout. The tool list placeholder denotes the tool set T, which contains all tools available for invocation.
Tool Definition and Usage This section provides detailed description of the visual tools inte- grated within our AdaReasoner framework. For each tool, we outline core functionality, input arguments, output format, and its specific role in addressing the challenges of our evaluation tasks.
POINT
Functionality: A perceptual tool designed for precise object localization. Given an image and a natural language description of target (e.g., "the start point", "red cars"), returns list of pixel coordinates (x, y) of the objects' center.
Role in VSP: This tool is fundamental for grounding the model in the spatial environment. In both Navigation and Verification, is the first step to accurately identify the locations of the start, goal, and hazardous ice holes, converting the visual grid into structured representation that can be used for planning.
DRAW2DPATH
Functionality: A visualization and verification tool. It takes starting coordinate and sequence directional commands (e.g., ['U", 'U', 'R') and overlays the corresponding path onto the input image.
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Role in VSP: This tool externalizes the model's internal plan into visual artifact. In Ver- ification, it renders the given path for the model to judge. In Navigation, it serves as a final check, allowing the model to visually confirm that its generated path is correct and safe before outputting the final answer.
ASTAR
Functionality: A classic planning algorithm encapsulated as tool. It computes the shortest obstacle-free frree path between start and goal coordinate, given the locations of obstacles.
Rolee in VSP: This tool offloads the complex pathfinding computation. After the POINT tool identifies all key locations, ASTAR can be invoked to generate an optimal, logically sound path, freeing the MLLM to focus on higher-level task management and verification.
DETECTBLACKAREA
specialized perception tool for the Jigsaw task. It analyzes image and returns the bounding box coordinates of any completely black, rectangular regions, which correspond to the missing puzzle piece.
Functionality:
Role in Jigsaw: This tool provides a deterministic way to identify the "problem space". is the critical first step in the solution trajectory, telling the model precisely where the candidate patches need to be inserted.
INSERTIMAGE
visual manipulation tool. takes a base image, patch image, and a set of coordinates, and returns new image where the patch has been inserted at the specified location.
Functionality:
Role in Jigsaw: This tool enables iterative hypothesis testing. The model uses it to physically place each candidate patch into the missing slot identified by DETECTBLACKKREA The resulting composite image is then fed back to the model, allowing it to visually assess the quality of the fit.
CROP
Functionality: An attentional tool. takes an image and bounding box coordinates and returns a new, smaller image containing only the specified region.
Role in GuiQA:
This tool mimics the human ability to focus interface. By cropping a region interest (e.g.. a button or a text block), the model can reduce noise and ambiguity, creating cleaner input for subsequent analysis by other tools or by the model itself.
specific part of dense
OCR
Functionality: An information extraction tool. It performs Optical Character Recognition input image, returning all detected text along with its corresponding bounding box coordinates.
Role in GUIQA: This tool grounds the model's understanding in the literal text present in the GUI. It is often used synergistically with CROP. After isolating key UI element, the model can use OCR to reliably read its label (e.g.. "Buy Now"), preventing the visual-only hallucinations common MLLMs.
B.2 HIGH-QUALITY COLD START TRAJECTORY DATA CURATION
For our structured reasoning tasks, developed data generation and trajectory creation pipelines to ensure high quality and diversity. Some detailed data samples are shown in appendix B.3
VSP The VSP benchmark environments were procedurally generated using the Gymma- sium (Towers et al. 2024) framework. To ensure variety, we systematically controlled the distribu- tion of start points, end points, and ice holes. We synthesized environments sizes 4 x 4, 6 x 6, and 8 8 for the training set, while reserving larger 5 5, 7 7, and 9 grids for testing. The Tool Cold Start (TC, SFT) trajectories were designed to mimic an optimal problem-solving process. For Navigation tasks, the trajectory consists of: (1) invoking the POINT tool localize the start,
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Step4: Verify Solution
You are maze solver. Your goal is to guide a player from the grid map while avoiding holes.
User:
Assistant: have successfully identified the Elf, the Gift. and all the Ice Holes. My goal is to find path from the Elf to the Gift, avoiding all ice holes. Given the grid structure and the relative positions, I'll test the path RR,DD. first atterpl to see if reaches the goal safety. I Ined to visualize this path to confirm its validity,=/thing <tool, call>>"name". "Draw2DPath" "parameter".: ("image": "img_ "start "t.'sart_poontt" [352.0,160.0]. 1.........0..ddrcctooosss "R.DDR.D.R.D.R.DD"" "pixel coordinate": true))+/tool call>
start to the goal
as
You can the following tools to help you finish this task: (Point), (DrawPath)
"directions":
Step1: Determine the start
Assistant: <think> I need to identify the starting point of the maze. will use the Point' tool to locate the coordinates of provided image,</thinks </think> <tool_ calli="namme: "Point", "parameters". ("image": "img_1",
User:
("tool response_ from":
'Draw2DPath".
"description: "=lo")) call>
"status": "success",
"messages": "Path drawn successfully." "error code": 0) <image?
User: : "Point". ("tool respoose_essee_____
from": "status": "sucoess".
"points": [["x": 352.0, "y": 160.0)),
'image dimensions "imensions_pixell":
Step5: Correct Solution and Verify
("widhh": 512, 112hhhihht" 512).
Assistant: The Draw2DPath tool has returndd an image showing the path "R,,,R,D,R, D. Upon reviewing the drawn path, I can clearly see that it does not reach the destination (Gift) and potentially crosses over an ice hole gets stuck. need re-evaluate the
"error_ "error_code":00 0)
Step2: Determine the end
will This
try the sequence
D,D,L,L,,,DD,,,,,,R,,,,,,
Assistant: <think<Now that have successfully located the Elf, my next step is find the destination. will the Point again to identify the coordinates of the Gift./thinke <tool_ call>-"name". "Point", "parameters": ("image": "img_1", "description: "Gift"))s/ool. call>
path attempts to navigate around the clusters ice holes and find clear passage. </hinkk>>
<tool_ call--"namee: 'Draw2DPath" "parameters": ("image":
"img 1", "tart point": [352.0. 0.60.0).660..... "directions":
".U.LL...D.DR...................
.......... .//ttooooo call>
User:
User:
("lool response, pesponse_from": from": "Point",
("tool responseeppeeeee response_from" 'Draw2DPath" "slatus": 'success", "messages": "Path drawn successfully."
"status": "success",
"points": [['x": 416.0, 352.0). "image dimensions, pitiiessions_ppeels" ("width":55 "he)ghf::12).
2
'error_ cotoo__coe:0000 0)
"error code": 0) <image>
Step3: Determine the Holes
Step6: Analysis and Response
Assistant: <think>1 have successfully identified the starting point (Elf) and destination (Gift). The crucial step to locate all the "lce Hales" the map, which represent obstacles that must be avoided.s/hinle> <tool_ call>""name" "Point', "parameters": ("image": "Img_1" "description": "Ice Holes")) call>
Assistant: The Draw2DPath tool successfully drawn the new path. Upon visual inspection of the image, the drawn path clearly starts through the open ice, avoids all the identified ice holes, and successfully reaches the Gift. This path is correct and achieves objective of guiding the player from slart finish while avoiding obstacles. I now have the final answer. </think> reepoee>boeeeLLLLLLDDoRR......
Elf's position, safely navigates
User:
("tool taa__resposs__eeoe from": "Point", "status": "sucoess",
"points": [["x": 32.0, 'y:: 32.0)),
<iresponse>
"points": [['": 160.0, "y": 480.0)). "image dimensions pixels":
("width::12, "55)ght":512).
"error "error_code":00 0)
Figure 6: An example of a multi-turn cold-start trajectory for the VSP task.
end, and all ice holes; (2) performing textual reasoning based on these coordinates; and (3) calling DRAW2DPATH for final verification. Crucially, we also incorporated reflection and backtracking data derived from failure cases. For Verification tasks, the trajectory involves: (1) using POINT to locate the start, (2) employing DRAW2DPATH to render the proposed path, and (3) prompting the model to judge if this path intersects any ice holes.
Jigsaw The Jigsaw dataset was constructed using images from the COCO-2017 (Lin et al. 2014) training set. Each instance was created by first dividing an image into 3 x grid. A x 2 sub- sub-grid was then selected the base image, from which one patch (e.g., top-right) was removed to create the problem. The removed patch served as the correct answer, while one of the remaining five patches from the original 3 3 grid was chosen distractor. The TC trajectory instructs the model to: (1)
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User: Giaa lins emaee will cane perfrouungg
which sxore, ae relerchinngg imageo per or ur 627
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1s 142. 213 201 Aoserting tho rigit unii corned snover frunt. inten by interinng sacoed image (ing. Shot paint hans". "roertrrgee "Jerurdenn"" Tleos mmag"" Imng 1", wtw.p............ ko 2887)
regaddeyy heed mext canditra image
An example of a multi-turn cold-start trajectory for the Jigsaw task. This trajectory showcases an iterative trial-and-eree process. The agent first uses the missing region. It then sequentially attempts to InsertImage with each candidate patch, analyzing the visual result of each attempt before arriving the correct solution.
Figure 7:
to identify
Step2: DCR
You visual posistiont capablee solving
Assistant:
ethinko- have succossffully cropped the specific area containing the "Pormanent link OUR Archiva version:" creating 'img 2", Now, I need to use OCR on this crapped mage extract the actual boxt of the link and ceternine if contims the ablitt to download the thess, <think-
reasoning problemms
Question: Can download the thesis on Music in the Science Classroom' from this websoage?
You can the following tools help finish this tiok:
<tool calix/hamm" "OCR" "parameters": ("image": 'img 21)-- call>
(Paint). (DrawPath)
User:
('ool Lresponse__eeee from": "OCR". "status"
Crop
"dotections" (Taaal:: "atp://hd/hancle 105/105223/2200 "oonfidenca.
=think-The The user askling wnither spealfic thesis, (MMusic the Science Classropm/" can be downloaded from the webpage. To answer this,
Assistant:
36, "proll bbax": (*x_ mmin". mir" 10,7, max": 200."y_mxx" 23)).
"mrage, diimersiooss sirpersioor_ppiells.... ("width": 201, "heigh"" 32).
need to find information on the page that idiiates dounicad avaliabillty such as Ink or downloed button. can see the thests bile displayed prominently. Below the MMMdaadnnn Tomi" author name, to the right of the thumanail
"erorr coce": 0) <maga>>
there section providing otalion detailss Withinn this secioon
Step3: Analyse and Rasponse
followed fink OUR Archive lazie by URL. phrase strongly suggests that
phrase
Assistant: <thinko The OCR tool suczessfuly extrecied the lext hhlp:ihhll.hm confirma
retrievsi and downioad. confimm nead focus on this spectfic area. have idertified the coordinates of the 'PPermment link?" text as [321,445, 522 <thinko
11052244200 from cropped image, permanent presence of such link indicates thosts lable the wabpage. that the thes can be downlocced <ithiii> cresponse> Yes, you can download the thesis on "Muscc the Science Claseroan' from this webpage. A permannett link (ttp::hdd neb 2d1106234403) the thesls provided <haasponse
<lpol call I"namee: 'Crop". "parametens": (" l'image": "Ima. I", "coardinatee""
[321,446, 522.4227))) call-
User:
("ooo resporseeseeeee from': "Crop". "status" "success",
"meseage": "Image cropped succeesfully
using absolute coordinates.. "image, dimersions_ _oreessssesssss" ('widtn: 201, "hight": 32).
http: handle net1102234400
"error code" 0) <magge>
Figure 8: An example of multi-turn cold-start trajectory for a GUI-QA task. This sample illus- trates a focus-then-extract strategy. The agent first uses the CROP tool to isolate a specific, relevant section of the webpage. It then applies the OCR tool to this cropped, unambiguous input to perform precise information extraction.
call DETECTBLACKARES to identify the coordinates of the missing section, and (2) iteratively call INSERTIM AGE for each candidate patch until the puzzle is solved. To enhance robustness and diver- sity, introduced several key variations: 1 The order of patch insertion attempts was randomized to ensure a uniform distribution of options. Scenarios involving tool failures (c.g., detection er- rors) were included, prompting the model to fall back on its intrinsic knowledge after several failed
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attempts. @ A proportion of samples designed to be solvable directly by the model without tool use, encouraging adaptive tool invocation.
The process begins with 44k single-turn instances from the Guichat dataset. (Chen al. 2024) To identify challenging cases that necessitate tool use, we first prompted a powerful vision- language model, Qwen-VL-2.5-72B (Bai al. 2025). to answer the questions. We retained only the instances where the model failed, resulting in a subset of 7,100 "hard" questions. Next, for these 7,100 instances, we rendered the ground-truth answer coordinates as bounding boxes on the images. We then performed a manual visual inspection to ensure these boxes contained meaningful and relevant information, which filtered the set down 1,800 valid data points. To generate high- fidelity tool-use trajectories for these cases, we provided the ground-truth answer and coordinates to semini-225-ffaa Comanici al. (2025), prompting it to produce the chain-of-thougll reasoning and tool invocation sequence required to solve the problem. Finally, all generated trajectories were validated against our predefined format, and only those that strictly conformed were retained. This final curation step yielded high-quality dataset of 1,139 instances for our TC stage.
GUIQA
After defining the abstract trajectory structure for all tasks, we followed unified, two-stage process to create the final training data. First, we executed these trajectories programmatically to populate them with real tool inputs and outputs. Subsequently, we leveraged Gemini 2.5 Flash (Comanici) al., 2025) to generate the corresponding chain-of-thought (CoT) reasoning for each step. This process resulted in final dataset of high-fidelity, tool col-augmented trajectories complete with explicit reasoning chains, ready for our cold-start training.
B.3 DATA SAMPLES
To provide more conerete understanding of our cold-start data, we present representative multi-tury trajectory samples for each of our core tasks in Figures 6 7 and These examples are designed to showcase the sophisticated, human-like reasoning patterns we aim to instill the model during the supervised fine-tuning phase.
The VSP sample (Figure (6) illustrates methodical, multi-stage problem-solving process that in- cludes perception, verification, and analysis. The Jigsaw sample (Figure 7 demonstrates iterative trial-and-error strategy, where the agent actively evaluates the outcome of each tool call. Finally, the GUIQA sample (Figure { highlights synergistic tool-use pattern, where one tool ("Crop') is used to create optimal conditions for another ('OCR'). Across all examples, the interplay between the model's 's internal thoughts ('<think>>) '), tool calls, and observations from the environment is clearly demonstrated.
B.4 TOOL GRPO
Group Relative Policy Optimization (GRPO) reinforcement learning algorithm that evaluates policy performance by directly comparing group of candidate reasoning trajectories. The process of Tool GRPO in AdaReasoner begins with the initial state = {g. text, image). for which the policy ne samples set of N complete trajectories as candidate responses, {rl , ...,,
.,... - }. Each
trajectory is evaluated by a reward function, yielding rewards pf = R(r'). GRPO then calculates a group-relative advantage A for each trajectory by normalizing its reward against the statistics of the entire group:
A' = r; mean
(raa (r,, 12, r2,...,,NN'
(3)
stdlfri.r2..... .,. TNN
The policy is then updated to favor trajectories with higher relative advantages by maximizing clipped surrogate objective function. This objective is designed to ensure stable training by prevent- ing excessively large policy updates. The full objective is:
JERPO (0) Bgo ~
(4)
Σ
G min Ai clip(s/,11 -E, 1+ ++))A.) B DKL (TO Tyat)
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=c(a))) is the importance sampling ratio that measures the change between the Here, m/ (
new policy wo and the old policy w0.te used to generate the samples. The Kullback-Leebler (KI.) divergence penalty, D (To (eet) regularizes the policy update by penalizing large deviations from a reference policy Tef--
Our reward function is designed to evaluate both the structural syntax and the semantic correctness of the model's output. The total reward, Riotal is composite score defined as:
Reward Design
(5) Here, Roomat acts as a binary gate, ensuring that rewards for tool usage (Rwol) and final answer accuracy (Raceurcy) are granted only if the output adheres the required structure. This design incentivizes the model to first master the correct syntax before optimizing for functional correctness.
Rysaal =
Rtormat (Awol Recot + Auce Race)
Aace is hyperparameter and the effect of it is discussed in Section
Format Reward (Roormat) The format reward is a binary signal that assesses the structural in- tegrity of the model's output. verifies that the generated response contains all required special tokens in the correct order and follows predefined rules.
{
if the output format is valid otherwise
Rioemat
(6)
Rforma is O, the total reward for the trajectory is nullified, creating a strong imperative for the model to learn the required output structure.
Tool Reward (Rwal) The tool reward provides a fine- fine-grained evaluation of the tool-calling pro- cess, with score ranging from 0 to 4. We employ hierarchical scoring system where each level must be passed to proceed to the next.
1. Invocation Structure (Score 1): A score of awarded if the tool call correctly encap- sulated within the <tool.call> and </tool.call> tokens. If not, the score is 0, and no further tool evaluation occurs.
Tool Name Validity (Score 2): If the structure correct, we verify that the invoked tool name exists in the set of available tools, T. If the name is valid, the score becomes 2.
3. Parameter Name Correctness (Score E [2, 3)): : If the tool name is valid, assess the parameter names. A partial score awarded based on the proportion of correctly named parameters, A perfect match yields score of 3. The score is calculated as:
|arrams correct
Rooo 2 +
params
4. Parameter Content Validity (Score e [3, 4)) Finally, if all parameter names are correct (base score of 3), we evaluate the parameter values for semantic and contextual validity. The final score is proportional to the number correct values, reaching a maximum
params correct content
RIoe 3
params
Accuracy Reward (Raccuracy) The accuracy reward evaluates the final outcome of the reasoning process, providing clear signal based on the correctness of the model's final answer.
if the final answer is correct otherwise
R Raccuracy
This multi-faceted reward structure guides the model toward not only achieving the correct final answer but also mastering the intermediate steps of correct formatting and precise tool invocation.
C EXPERIMENT DETAILS
C.I TASK DEFINITION
We evaluate our approach across a diverse suite of three challenging tasks to validate whether our approach can help enhance the reasoning ability.
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Visual Spatial Planning We adopt the FrozenLake scenario (Towers et al.) 2024) to evaluate mod- els' spatial planning and verification abilities. The navigation task requires the model to generate safe path from the start to the goal while avoiding holes, which demands accurate perception of the grid map and robust sequential reasoning to plan multi-step trajectories. The verification task instead focuses state checking, determining whether given location proposed path is safe, which isolates the perception and reasoning components of the planning pipeline. Together, these tasks expose two fundamental challenges for VLMs: (i) precise visual perception of spatial layouts under varying map sizes, and (ii) reliable reasoning over action sequences to ensure safety and goal completion.
Concretely, we evaluate models on two benchmarks. The first is VSPO, a dataset we construct to assess out-of-distribution generalization. During training, we use maps of sizes 4 x 4, 6 x 6, and 8 x 8, while reserving maps of sizes 3 x 3, 5 x 5, 7 x 7, and 9 x 9 for testing. This setup not only probes the model's s ability to generalize to unseen spatial configurations but also examines whether truly learns to leverage tool usage for problem solving. The second is the original VSP benchmark (Wu et al. 2024), where we adopt the navigation and verification tasks to further test visual-spatial reasoning and clate-checking capabilities under standardized settings.
Jigsaw The Jigsaw task (Noroozi & Favaro 2016) evaluates a model's ability to reconstruct holis- tic understanding from fragmented visual inputs. Specifically, the model must infer the correct spatial arrangement of shuffled image patches and reason about their part-whole relationships. This requires fine-graine -raingd perception to capture local details, as well as global reasoning to integrate them into a coherent whole. The key challenges lie in bridging local-globil consistency and maintaining semantic alignment across patches, making it strong test of visual compositionality and structural reasoning.
Concretely, we evaluate models on two benchmarks. The first Jigsaw-COCO. where we construct training and test splits based on the COCO train 2017 dataset (Lin et al. 2014). We extract the top- left, top-right, and bottom-left patches of each image to form the training set, while reserving the bottom-right patches for testing. This design allows us to probe the model's out-of-distribution generalization and examine whether it truly learns to invoke tool usage for solving the puzzle. The second is the Jigsaw benchmark from BLINK (BLINK-J) (Fu et al. 2024). which provides standardized evaluation of fine-grained visual reasoning and compositional understanding under more challenging and diverse settings.
The GUIQA task is designed to evaluate a model's sophisticated capabilities in fine- grained visual understanding and critical information extraction from GUIs. In this task, model is provided with a GUI screenshot and an associated question, where the main difficulty lies in precisely grounding UT elements on dense layout, comprehending their functional roles, and per- forming multi-step reasoning by integrating scattered information.
GUIQA
The evaluation conducted on two distinct datasets. The first is the GUIChat (Chen et al. 2024), which specifically probes the model's capacity for interactive, dialogue-based comprehension of webpage screenshots. Models are required to process complex queries related to visual information, human-centric needs, world knowledge, and reasoning. The second is the WebQA from the Web- MMU (Awal et al., 2025). It offers a structured evaluation across three distinct categories. Agentic Action tests the ability to understand UI elements like buttons and menus in order to formulate the necessary user actions, complete with precise spatial grounding. General Visual Comprehension assesses how well the model can extract and synthesize information from varied page components, including text, images, and graphics. And Multi-step Reasoning demands complex inference, nu- merical calculations, and comparisons across different parts of the UI.
C.2 PROMPTS
The system prompt used for guiding the tool-planning model is provided in Figure 9
C.3
IMPLEMENTATION DETAILS
We developed Tool Factory an end-to-end framework that orchestrates the entire lifecycle of our tool-planning models, from data curation to evaluation. At the heart of this framework is the Tool
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System Tool Prompt
You are a visual assistant capable of solving visual reasoning problems. You can rely on external tools to assist in solving- Available Tools In your response, you can use the following tools:
your own capabilities
{Tool List] Steps for Each Turn
Think: First, silently analyze the user's request to understand the goal. This thinking process should be enclosed in <think> and </think> tags.
Decide Action: Based on your thinking, decide on one of the following two actions:
If you need to use tool: Generate your tool call, enclosed between <tool_call 1> and </tool_call> tags. Do not generate a <response> in this turn.
If you have enough information to answer: Generate your final, user-facing an- swer, enclosed between <response> and </response> tags. Do not generate <tool_call> in this turn.
Output Format:
Your output must always begin with your thought process. After the <think> block, you must provide either a <tool_cal or <response>, but never both in the same turn.
Case 1: Tool Use is Required
<think> Your thoughts and reasoning </think>
<tool call>
("name": "Tool name", "parameters": ("Parameter name": "Parameter content", "..": "..."})
</tool.call>
Case 2: Ready Respond to the User <think> Your thoughts and reasoning </think> <response> Your final response </response> Important Notes
1. You must always include the <think> field to outline your reasoning. Provide one of <tool_call> or <response>. You must include both <tool_call> and <response> in the same turn because they are mutually exclusive. If tool usage required, you must instead include both <think> and <tool_call>, and omit <response> for that turn. If no further tool usage is required and ready to answer the user's question, you can then use <think> <response> with your final answer, and this indicates the ends of the conversation.
summarize your reasoning and include
You can only invoke single tool call time in the <tool_call> fields. The tool call should be JSON object with "name" field and a "parameters" field containing dictionary of parameters.
no parameters are needed, leave the "parameters" field an empty dictionary. All images have their coordinate origin at the top-left corner.
3. Some tools require image input. You do not need to generate or upload the actual image data simply refer to image using placeholder in the form of "img.n". There may be multiple images present in the dialogue. Besides the original image, additional images may appear result of prior tool calls (e.g., edited images returned by visual editing tools). You are free to select which image to use as input for the next tool. The index in "img refers to the image's 's position in the dialogue history:
The original image is always referred to as "img 1".
Each subsequent image, including those returned from tools, is assigned "img.2" 2", "img_3", and so in the order they appear in the dialogue.
For example: { "parameters": ("image": "img-l", 4. All image coordinates used must be in absolute pixel values, not relative normalized coordinates.
"other Nother_values")
5. At the end, provide your final answer by placing it inside boxed[], and wrap the entire final output inside <response></responee tags.
Figure 9: Our system employs tool prompts to guide models in learning how to use tools effectively.
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Table 7: Tool Cold Start (SFT) Training Configuration and Hyperparameters.
Category
Hyperparameter
Value Setting
Base Model
-e.-n2..-VL-BBBAB- I-IInuctuct
Vision Tower Frozen
True
Model
MM Projector Frozen Finctuning Type
True
Full
DeepSpeed Stage
ZeRO-3
Max Samples
332,649
Dataset
Cutoff Length
35,536
Preprocessing Workers
64
Batch Size Device
Gradient Accumulation Steps
Effective Batch Size
Training
Learning Rate
Ic-5
Epochs
3
LR Scheduler
cosine
Warmup Ratio
0.1
Mixed Precision
6
Logging Steps
10
Logging / 10
Checkpoint Save Steps
Train/Validatie Split Eval Batch Size per Device
90% 10%
Evaluation
Eval Steps
100
Server, a unified, MCP-like service that manages available tools, from simple offline utilities to compute-heavy online expert models.
C.3.1 DATA CURATION
During the data curation stage, we employ our Tool Curation Module, which leverages the Tool Server to automatically generate high-quality cold-start trajectories. Specifically, we first design abstract, optimal problem-solving blueprints for each task, consisting of a tool-call chain and chain- of-thought (CoT) placeholders. We then prompt Gemini-2.5-Flash to fill these placeholders with detailed CoT reasoning. Finally, the Tool Server executes the corresponding tool calls and integrates the results into the dialogue, yielding a complete and coherent training instance.
C.3.2 TOOL COLD START STAGE
During the cold-start stage, these trajectories are used for full-parameter supervised fine-tuning, for which we adopt the LLaMA Factory framework (Zheng et al. 2024). The key configurations and hyperparameters are summarized in Table 7
C.3.3 TOOL GRPO STAGE
Following SFT, the model is further refined in the Tool GRPO stage using TooIRL, our custom inforcement learning framework inspired by [Sheng et al. (202)); Zheng et al. (2025), which also relies on the Tool Server for live tool interactions. Finally, for systematic performance assessment, we developed TF-Eval, our dedicated evaluation framework. Ev-Eva interacts with the Tool Server to benchmark tool-planning models across diverse suite of multimodal tasks. The key config- urations and hyperparameters of Tool GRPO stage are summarized in Table
D DISCUSSION
central finding of our work concerns the dual role the Tool-Cold-Start. (SFT) phase, which highlights critical trade-off between imparting expert knowledge and preserving model's
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Table Key configurations and hyperparameters used in the Tool GRPO stage.
Category
Hyperparameter
Value / Setting
Max Prompt Length
8192 tokens
Max Response Length
20480 tokens
Data
Train Batch Size
32
Shuffie
True
Filtr Overlong Prompts
True
Strategy
FSDP
Gradient Checkpointing
True
PPO Mini-batch Size
8
PPO Micro-hatch Size GPU
1
Max Token Len GPU (PPO)
16384
Grad Clip
1.0
Policy
Clip Ratio (PPO)
0.2
PPO Epochs
I
Entropy Coeff'
0.0
Use KL Loss
False
Actor LR
Ic-6
Weight Decay
0.01
FSDP Param Offload
True
FSDP Optimizer Offlod
True
# Nodes / GPUs
I node, 8 GPUs
Engine
vLLM
Temperature
1.0
Top-p
1.0
Top-k
-1
Samples per Prompt (n)
4
Rollout
Dtype
bfloat16
Tensor Model Parallel Size
2
Max # Batched Tokens
32768
GPU Memory Utilization
0.65
Enforce Eager
False
Chunked Prefill
False
Tool-Agent
Max Turns per Episode
Strategy
FSDP
LR
le-5
Weight Decay
0.01
Critic
PPO Epochs
1
Grad Clip
1.0
Advantage Estimator
GRPO
Gamma
1.0
Lambda
1.0
Algorithm
Use KL in Reward
False
KL Coef
0.0
Norm Adv by Std in GRPO
True
exploratory freedom. Our results suggest that the decision to include a supervised pre-trate pre-training stage is not universally beneficial, but rather highly contingent on the nature of the task.
For complex, structured tasks with discernible optimal solutions, such as VSP and Jigsaw, the SFT phase provides a decisive advantage. In these scenarios, discovering an effective tool-use trajec- tory from scratch is a non-trivial exploration problem for the model due to its inherent reasoning or knowledge deficits. By exposing the model to high-quality, deterministic solution paths, the Tcol-Cold-Start phase effectively bootstraps the learning process, instilling a strong inductive bias towards a correct strategy. The empirical results in Table validate this unequivocally: for our 7B models, adding this SFT phase before Tool-GRPO yields a massive performance improvement of +24.93 points on VSP and +19.82 points Jigsaw compared using Tool-GRPO alone.
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Published as a conference paper at ICLR 2026
Conversely, for open-ended and highly generalized domains like GUIQA, the limitations of this pre- defined guidance become apparent. In such settings, the optimal tool-use strategy is often unknown to human designers, making any human- designed trajectory likely sub-optimal. We find that a rigid SFT phase can inadvertently restrict the model's exploratory freedom during subsequent RI. by creating strong policy bias, which hinders the discovery of more effective, emergent strategies. This effect is clearly observed in our results for the 7B model on the WebMMU benchmark, where the standalone Tool-GRPO approach actually outperforms the combined pipeline (72.97 68.16).
This dichotomy suggests a key principle for training tool-augmented agents: while injecting expert knowledge via SFT is powerful method for tasks with well-defined solution spaces, a pure rein- forcement learning approach like Tool- may be superior for more dynamic and general tasks that benefit from unconstrained exploration.
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Reviews:
[{"review_id": "bwJqQ7xhEL", "reviewer": "Reviewer_bExz", "summary": "This paper presents AdaReasoner, a framework designed to enhance the reasoning capabilities of Multimodal Large Language Models (MLLMs) through dynamic tool orchestration. The method combines two stages: a Tool Cold Start phase that generates and trains on high-quality, multi-turn trajectories, and a Tool-GRPO reinforcement learning phase that refines tool-calling strategies using adaptive rewards. The system enables MLLMs to autonomously plan, select, and modulate tool usage across perception, manipulation, and planning tools, thereby improving performance on complex visual reasoning tasks.\n\nEmpirical results are striking—on benchmarks such as Visual Spatial Planning (VSP), Jigsaw, and GUI Question Answering (GUIQA), AdaReasoner achieves significant improvements over strong baselines. The 7B model, for example, reaches 97.6% accuracy on VSP, surpassing proprietary systems like GPT-5 and Claude Sonnet 4. The work advances the notion that smaller open-source models, when augmented with dynamic tool-use capabilities, can achieve reasoning performance comparable to far larger models.\n\nThe paper makes a valuable empirical contribution and is well executed. The results convincingly demonstrate that structured trajectory learning and tool orchestration can significantly enhance reasoning performance in MLLMs. The writing is clear, the motivation is sound, and the overall system engineering is strong.\n\nHowever, the core methodology does not introduce substantial novelty, and the dependence on handcrafted trajectories and domain-specific tools limits generality. The framework, while powerful, feels more like a carefully engineered pipeline than a broadly applicable reasoning paradigm. The claims about emergent behavior and scale independence would benefit from more controlled evidence and broader validation.\n\nWith a reframed emphasis on the strength of the trajectory-based supervision and a deeper analysis of generalization and statistical robustness, this work could evolve into an impactful contribution. In its current form, it falls slightly short of the threshold for acceptance due to limited methodological originality and questions about scalability and generality.", "questions": "How scalable is the manual trajectory design process? What level of effort is required to produce the abstract blueprints for a new task?\n\nHave experiments been conducted using only general purpose tools, removing highly task specific ones, to test the limits of generalization?\n\nCan the framework extend to less structured or open ended reasoning tasks beyond spatial or puzzle-based problems?\n\nIn the adaptive behavior analysis, how much of the improvement stems from the Cold Start initialization versus the RL fine-tuning?\n\nWould training runs with identical data but without the RL phase help isolate the true contribution of Tool-GRPO?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper addresses an important and timely challenge in multimodal AI, how to move beyond single-tool usage toward adaptive, multi-step tool coordination. The problem is clearly defined, the motivation is well grounded, and the proposed framework is logically structured. The design combining curated multi-turn trajectories with reinforcement learning represents a meaningful step toward more adaptive and interpretable reasoning systems.\n\nThe writing is exceptionally clear and the presentation is of high quality. The figures effectively convey both the framework’s architecture and the emergent behaviors observed during training. The methodology section provides sufficient detail for replication, and the promise to release code and datasets demonstrates a commendable commitment to reproducibility.\n\nExperimentally, the paper is strong. The benchmarks cover a variety of structured reasoning settings, and the comparisons include both open-source and proprietary systems. The improvements obtained on complex multimodal tasks are substantial and consistent. Analyses showing how the model learns to adopt or discard tools based on task context add credibility to the claim of adaptive reasoning. The data curation process is also thoughtfully designed: by deliberately including reflection, backtracking, and tool-failure cases, the authors teach the model robust error recovery and self-correction behaviors that go beyond standard supervised fine-tuning.", "weaknesses": "While the empirical results are impressive, the methodological contribution is incremental. The proposed Tool-GRPO is effectively an application of existing GRPO with customized reward shaping and formatting constraints. The novelty lies primarily in system integration and data engineering rather than in algorithmic or theoretical innovation.\n\nA second limitation is the heavy reliance on manual, task-specific design. The “abstract problem-solving blueprints” that underpin the Cold Start data are crafted by hand for each task, and the tool suite itself includes several domain-specific utilities such as DETECTBLACKAREA or ASTAR. This reliance raises concerns about scalability and whether the framework could generalize to new or less structured domains without similar levels of manual effort.\n\nThe claim of “emergent” or “self-adaptive” behavior is also somewhat overstated. Much of the observed adaptivity appears to stem from the explicit supervision provided during the Cold Start stage rather than from genuine spontaneous generalization. Moreover, the paper lacks key elements of experimental rigor such as no variance estimates, error bars, or significance testing are reported, which limits confidence in the stability of the reported gains. Finally, the “tools-over-scale” conclusion, though intriguing, is demonstrated primarily on highly structured tasks where specialized tools contribute most of the lift, leaving questions about its broader applicability.", "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": "pcqZZG5GIp", "reviewer": "Reviewer_Yfxj", "summary": "This paper proposes the AdaReasoner framework, a two-stage paradigm (Cold Start: high-quality multi-turn tool trajectory curation; Tool GRPO: multi-turn tool reinforcement learning optimization) that teaches MLLMs to dynamically combine 7 core visual tools (perception, manipulation, calculation) for iterative visual reasoning. It boosts performance on VSP, Jigsaw, GUIQA (e.g., 7B model: +38.7% avg, 97.6% VSP accuracy), outperforming GPT-5. The model adapts to new tools (e.g., ASTAR) during inference, adopting beneficial tools and discarding irrelevant ones, but has limitations.", "questions": "- The paper only verifies the adaptability of \"calculation new tools (e.g., ASTAR for pathfinding)\" during inference, without involving \"perceptual tools (e.g., a newly added high-precision segmentation tool)\" or \"manipulation tools (e.g., a newly added image rotation tool ROTATE)\". If such new tools with different functions are introduced during inference, can the model still maintain its zero-shot adaptation ability? Are there differences in the adaptation difficulty among new tools of different functions (e.g., lower adaptation rates for perceptual tools due to more complex parameters)?\n- The current toolset in the paper only includes 7 core tools. If two or more new tools are introduced simultaneously during inference, can the model autonomously distinguish the relevance between tools and tasks (e.g., using ASTAR only for navigation tasks and the new tool for 3D localization tasks), or will it experience tool selection confusion?\n- After training the model to master the combination strategy of \"POINT + ASTAR + DRAW2DPATH\" on the \"grid VSP task\", the paper does not directly test the model’s tool combination performance on \"similar indoor maze navigation tasks\" (which require obstacle localization, path planning, and use the same toolset). The absence of this experiment makes it impossible to determine whether the tool combination strategy learned by the model is \"task-specific\" (usable only in the trained task) or \"generally transferable\" (adaptable to similar tasks).", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- A key limitation of the \"rule-based reward structure\" in R1-style methods is that it primarily optimizes the reasoning process and fails to directly improve the model’s underlying perceptual capabilities. AdaReasoner directly addresses this shortcoming: by leveraging the precise perceptual capabilities of external expert models and specialized tools, it ensures high-fidelity understanding of visual inputs, thereby enhancing the reliability of the entire reasoning pipeline.\n- Unlike previous methods that typically focus on single-step actions, AdaReasoner explores more complex challenges such as \"multi-turn planning\" and \"dynamic tool composition\".", "weaknesses": "The most notable weakness of this paper lies in the limitations of evaluating tool generalization ability, specifically the \"oversimplified verification of new tools during inference\" and \"lack of adaptation to tool complexity\". These limitations cast doubt on the generalizability of the research conclusions in more complex and diverse tool scenarios.\n\nSee other Weaknesses in Questions.", "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": "xMVwQgn5Sh", "reviewer": "Reviewer_W5WP", "summary": "This paper introduces AdaReasoner, a framework for dynamic tool orchestration in MLLMs. Unlike prior approaches that rely on fixed, single-step tools, AdaReasoner allows models to plan, select, and compose tools adaptively across multiple reasoning turns. The framework combines: 1. a data curation pipeline that produces multi-turn tool-use trajectories with reflection and backtracking, and 2. a Tool-GRPO reinforcement learning algorithm designed to optimize these multi-turn trajectories with adaptive rewards.\nEmpirically, AdaReasoner significantly boosts reasoning accuracy of open-source vision–language models (e.g., Qwen2.5-VL-7B) across tasks like Visual Spatial Planning, Jigsaw, and GUI-QA, achieving up to +38.7% average improvement and even outperforming large proprietary models such as GPT-5 and Claude Sonnet 4. The authors also report emergent adaptive behaviors, where the model autonomously learns to adopt or discard tools depending on task context.", "questions": "For proprietary model baselines, could the authors give more specific details on they were run? Were they prompted to answer in one turn? If the proprietary models were evaluated using frameworks that involve explicit multistep planning and reasoning, e.g. ReAct (https://arxiv.org/abs/2210.03629) or OctoTools (https://arxiv.org/abs/2502.11271), how would their performance compare to TC and TG methods? \nCould the authors clarify on how they are using the term \"emergent\" behavior, strategies, etc (see Weaknesses)?\nAre there ablation experiments varying the amount of compute / date used in cold-start affect downstream performance (with and without RL)? \nCould the authors include more details on training, e.g. hyperparameters?\nWill the cold-start trajectory dataset be released upon acceptance?\nMinor: figure 2 bottom panel is labeled (c) instead of (b)", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The authors demonstrate on their method improves over a variety of baselines across several visual reasoning benchmarks, with sufficient ablation experiments as well. A common limitation about training-based approaches for tool integrated reasoning is that they may not generalize to introduced tools. The authors address this by showing that at inference time, adding an unseen tool (A*) improves performance.", "weaknesses": "The authors show that RL training allows the model to learn how much to use different tools (\"adopt\", \"discard\", \"modulate\") and call this an emergent behavior at multiple points throughout the paper. However, the way the authors use the term \"emergent behavior\" could benefit from some clarification / definition. Generally, emergent behaviors refer to nonobvious / surprising capabilities not explicitly optimized in the object and generally only \"emerge\" at scale. In this case, the method is deliberately training the model to perform tool calls (with cold start and GRPO). Maybe learning adaptive tool selection, tool use optimization, etc are more precise terms to be used in this setting.", "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": "BJahdPchFH", "reviewer": "Reviewer_iRNU", "summary": "The paper “AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning” presents a new framework for enhancing multimodal large language models (MLLMs) with adaptive, multi-turn tool-use capabilities. Existing methods generally employ fixed, single-tool reasoning or scripted tool invocation, limiting their capacity for dynamic, multi-step planning. To address this, the authors introduce AdaReasoner, a system that integrates a diverse suite of visual tools and introduces a specialized Tool GRPO reinforcement learning algorithm alongside a Tool Cold Start phase for data-driven initialization. The system uses a curated dataset of reasoning trajectories that include reflection, backtracking, and tool failure cases—teaching models to plan adaptively and recover from errors. Extensive experiments demonstrate that AdaReasoner significantly improves reasoning performance, achieving +38.7% average accuracy gains on 7B models and near-perfect accuracy (97.6%) on Visual Spatial Planning tasks—outperforming both open and proprietary systems like GPT-5 and Claude Sonnet 4. The framework also exhibits emergent behaviors, such as autonomously adopting or discarding tools depending on their utility.", "questions": "please check above", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "The paper is well-motivated and grounded in both conceptual and empirical depth. It identifies a clear and timely gap in MLLM research—static or single-tool reasoning—and develops a complete system that addresses it with a mix of structured methodology and adaptive reinforcement learning. The data curation pipeline (with reflection and failure handling) is a major strength, as it systematically teaches models resilience and iterative self-correction. The Tool GRPO formulation introduces a carefully designed reward structure that balances correctness, formatting, and tool-usage efficiency, enabling stable optimization of multi-turn trajectories. The ablation studies and visualization of tool-call frequency provide valuable insight into the model’s learning dynamics, confirming the emergence of adaptive behaviors such as adopting useful tools (A*) or discarding irrelevant ones. Furthermore, the analysis connecting perception tools (“see”), manipulation tools (“verify”), and planning tools (“calculate”) adds interpretability and conceptual clarity. Overall, the work is good, empirically convincing, and establishes AdaReasoner as a strong contribution to multimodal tool-use research.", "weaknesses": "the paper leaves several conceptual and methodological aspects underexplored that could make the study more complete. Although the reward function and reflection-based trajectories are described, their quantitative sensitivity (e.g., how different reward weights or reflection ratios affect learning) is not studied, limiting reproducibility insights. While performance improvements are substantial, the evaluation scope is confined to visual reasoning tasks, and cross-modal generalization (e.g., audio or temporal modalities) is not examined, despite the framework’s claimed generality.", "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)