SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models
Authors: Hongxing Li
Dingming Li
Zixuan Wang
Yuchen Yan
Hang Wu
Wenqi Zhang
Yongliang Shen
Weiming Lu
Jun Xiao
Yueting Zhuang
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language Models
Spatial Reasoning
Primary area: foundation or frontier models, including LLMs

Abstract: Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We identify that this limitation stems from a critical gap: existing methods attempt to learn spatial reasoning directly without establishing the hierarchical foundations of perception and understanding. To address this challenge, we present a comprehensive methodology for building spatial intelligence progressively. We introduce SpatialLadder-26k, a multimodal dataset containing 26,610 samples spanning object localization, single-image, multi-view, and video spatial reasoning tasks, constructed through a standardized pipeline that ensures systematic coverage across modalities. Building on this dataset, we design a three-stage progressive training framework that (1) establishes spatial perception through object localization, (2) develops spatial understanding through multi-dimensional spatial tasks, and (3) strengthens complex reasoning via reinforcement learning with verifiable rewards. This approach yields SpatialLadder, a 3B-parameter model that achieves state-of-the-art performance on spatial reasoning benchmarks, with 23.4% average improvement over the base model, surpassing GPT-4o by 20.8% and Gemini-2.0-Flash by 10.1%. Notably, SpatialLadder maintains strong generalization with 7.2% improvement on out-of-domain benchmarks, demonstrating that progressive training from perception to reasoning is essential for robust spatial intelligence.

Published as a conference paper at ICLR 2026
SPADIALLADDER: PROGRESSIVE TRAINING FOR
SPATIAL REASONING IN VISION-LANGUAGE MODELS
Hongxing Li
Dingming Li
Zixuan Wang',
Yuchen Yan'
Hang Wu' Yueting Zhuang
Wenqi Zhang' Yongliang Shen
Weiming Lu
Jun Xiao
'Zhejiang University
fhongxing.12 sy1fezju.edu.cm
2 GitHub: http://hhuub..cmm////tAAAA////// Ladder
Project:
http:///jporrall.gtttb...o wttp://....oooatataaaaaaaaaaaaaa Ladder
ABSTRACT
Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We identify that this limitation stems from a critical gap: existing methods attempt to learn spatial reasoning directly without establishing the hierarchical foundations of perception and understanding. To address this challenge, we present comprehensive methodology for building spatial intelligence progressively. We introduce SpatialLadder--66. multimodal dataset containing 26,610 samples spanning object localization, sinle-image, multi- view, and video spatial reasoning tasks, constructed through standardized pipeline that ensures systematic coverage across modalities. Building on this dataset, we design three-stage progressive training framework that (I) establishes spatial perception through object localization, (2) develops spatial understanding through multi-dimeesional spatial tasks, and (3) strengthens complex reasoning via reinforcement learning with verifiable rewards. This approach yields Spa- tialLadder, a 3B-parameter model that achieves state-of-the-ae performance on spatial reasoning benchmarks, with 23.4% average improvement over the base model, surpassing GPT-4o by 20.8% and Gemini-2.0- 2.0-Flash by 10.1%. Notably, SpatialLadder maintains strong generalization with 7.2% improvement out-of- domain benchmarks, demonstrating that progressive training from perception to reasoning essential for robust spatial intelligence.
INTRODUCTION
VLMs have achieved remarkable success in fundamental visual tasks (Huang et al., 2025; Yu et al., 2025), yet critical capability remains elusive: spatial reasoning. While humans effortlessly grasp spatial relationships in visual scenes, current VLMs struggle even basic spatial queries (Yang et al., 2025a; Tong et al., 2024; Wu al., 2025b). This limitation severely constrains their deployment in applications requiring spatial intelligence, from robotics navigation (Zitkovich et al., 2023) to autonomous driving (Tian et al., 2024) and virtual reality systems (Chandrasegarran et al., 2024).
The root cause of this spatial reasoning deficit lies in fundamental gap between perception and reasoning in current VLM architectures (Chen et al., 2025; Li al., 2025c). We hypothesize that existing approaches fail because they treat spatial reasoning as monolithic capability, attempting learn directly from question-answer pairs without establishing the necessary hierarchical structure (Ouyang et al., 2025; Wu et al., 2025a). To validate this hypothesis, we conducted controlled experiments with 200 spatial orientation tasks, progressively adding perceptual hints to isolate the bottleneck (detailed in Appendix A). As shown in Figure 1, providing location hints (bounding boxes) improves accuracy by 5.0%, and additional directional cues yield another 4.5% gain, achieving 9.5% total improvement. This demonstrates that models possess latent reasoning capabilities but lack the perceptual grounding to activate them effectively. The primary bottleneck lies not in reasoning capacity but in the integration between perception and reasoning.
Current approaches to enhancing spatial reasoning in VLMs suffer from two fundamental limita- tions. First, existing datasets fragmented and narrow in scope, focusing on either 2D images or

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Figure perception-reasonee gap in spatial reasoning. Left: Three experimental conditions with increasing perceptual hints: no hints, location hints (bounding boxes), and full hints (boxes plus directional cues). Right: While Qwen2.5-VL-3B shows progressive improvement with increasing hints, our trained model achieves superior performance with negligible reliance external prompts.
3D scenes in isolation (Liao et al., 2025; Ouyang et al., 2025; Kamath al., 2023), while lacking systematic coverage across modalities and standardized annotation pipelines, resulting in incomplete training signals for comprehensive spatial understanding. Second, recent methods attempt directly optimize reasoning outputs through reinforcement learning (Liao et al., 2025; Ouyang et al., 2025; Wu et al., 2025c) auxiliary 3D representations (Wu al., 2025a; Hong al., 2023; Zhu al., 2024), without establishing the hierarchical structure required for spatial intelligence: they bypass the critical progression from perceiving objects to understanding spatial relationships to performing logical inference, producing models that memorize patterns rather than develop genuine spatial understanding, leading to poor generalization on novel spatial configurations.
We address these challenges through a systematic approach based on the hierarchical nature of spatial intelligence. Our key insight is that robust spatial reasoning must be built progressively: establishing perceptual foundations through object localization, developing spatial understanding through multi-dimensional spatial analysis, and ultimately achieving complex reasoning through their integration.
To implement this vision, we introduce SpatialLadder-2665 a comprehensive multimodal dataset containing 26,610 samples across four complementary task categories: object localization (5,929 samples), single-image spatial reasoning (5,929 samples), multi-view spatial reasoning (5,752 samples), and video spatial reasoning (9,000 samples). Unlike existing datasets, SpatialLadder-200 systematically covers the full spectrum from basic perception to complex reasoning. We develop a standardized pipeline leveraging 3D scene reconstructions from ScanNet to ensure consistent, high- quality annotations across all modalities.
Building on this dataset, we design three-stage progressive training framework. Stage I establishes spatial perception through object localization tasks, teaching models to accurately identify and locate objects scenes. Stage develops spatial understanding through multi-dimensional tasks including size estimation, distance judgment, and orientation analysis across seven distinct spatial dimensions. Stage 3 employs Group Relative Policy Optimization (GRPO) (Shao et al., 2024) with task specific verifiable reward functions to strengthen complex reasoning capabilities, enabling models to form coherent chains of spatial thought.
Through this progressive approach, we develop SpatialLadder, 3B-parameter model that es- tablishes new benchmarks in spatial reasoning performance. Extensive experiments demonstrate significant improvements: on VSI-Bench (Yang al., 2025a), SpatialLadder achieves 45.7% accuracy. On our proposed SPBench-SI and SPBench-MV benchmarks, it attains 70.2% and 70.9% accuracy respectively. Across all benchmarks, SpatialLadder achieves overall performance of 62.3%, surpassing the base model by 23.4% and outperforming GPT-4o by 20.8% and Gemini- Flash by 10.15 1... Crucially, Spatiall adder maintains strong generalization with 7.2% average
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improvement out-of-domain benchmarks including CV-Bench (Tong et al., 2024). SPAR (Zhang al., 2025). ViewSpatial- (Li et al., 2025a), MMSI-Bench (Yang et al., 2025b) and MindCube (Yin et al., 2025), demonstrating the robustness of our progressive training approach.
Our contributions are threefold:
We introduce SpatialLadder-26k. a comprchensive multimodal dataset with 26,610 samples spanning object localization and spatial reasoning across single-image, multi-view and video modalities, constructed through standardized pipeline ensuring systematic coverage and high-quality annotations.
We design three-stage progressive training framework that systematically builds spatial reasoning capabilities by establishing perceptual foundations, developing spatial un- derstanding, and strengthening complex reasoning through reinforcement learning with verifiable rewards.
We demonstrate that approach yields significant performance improvements, with SpatialL achieving state-of-the-ard results on multiple benchmarks while maintaining strong generalization to out-of-domain tasks, validating the effectiveness of progressive spatial learning.
RELATED WORKS
2.1 VISUAL. SPATIAL REASONING
As a key capability of VLMs, visual spatial reasoning is more complex than general visual tasks and remains challenging (Yang et al., 2025a; Wu et al., 2025b). Despite notable advances in basic visual tasks (Li et al., 2024a;b; Tang et al., 2025a;b), extensive benchmark (Yang et al., 2025a; Wu et al., 2025b) evaluations demonstrate that they still face serious bottlenecks in spatial reasoning. Recent studies have attempted to explore multiple remedies, such as RI RI-Zero-Va (Liao et al., 2025) and SpaceR (Ouyang et al., 2025), which utilize reinforeement learning enhance models' spatial reasoning capabilities; Spatial-MILLM (Wu et al., 2025a), which introduces 3D representations (Wang et al., 2025a) bridging knowledge; and Coarse Correspondences (Liu al., 2025a), which improves models' spatiotemporal modeling capabilities through cross-frame object tracking. However, there remains general lack of comprehensive, diverse, high-quality datasets, as well as effective training frameworks that advance from basic to complex concepts, to systematically enhance the capabilities VLMs in spatial reasoning tasks.
2.2 REINFORCEMENT LEARNING IN VLMS
Recent studies have extended Reinforcement Learning (RL) techniques from LLMs to VLMs, leading to notable progress in visual reasoning (Liu al., 2025b; Shen al., 2025). Representative works such as Vision-RI (Huang et al., 2025), MM-Eureka (Meng et al., 2025), and R1- Vievin (Yang et al., 2025c) have demonstrated that transferring RL methods significantly enhance the visual mathematical reasoning capabilities. In the video domain, Video- RI (Feng et al., 2025) and VideoChat-RI (Li al., 2025b) applied RI. to improve temporal understanding and video localization performance. Beyond text-oriented reasoning, methods like GRIT (Fan et al., 2025) and Pixel-Reasoner (Su et al., 2025a) leveraged RL stimulate "thinking with images" (Su et al., 2025b), enabling models to perform structured and interpretable multimodal reasoning. Despite these advancements, research specifically targeting visual spatial reasoning remains limited. To address this gap, propose a training paradigm that systematically enhances VLMs' capabilities in spatial reasoning tasks.
VLMs can
3 METHODS
We present comprehensive framework that systematically builds spatial reasoning in VLMs via progressive training. Our approach consists of two core components: (1) SpatialLadder- 26k. a multimodal dataset systematically spanning spatial tasks from basic perception to complex reasoning, and (2) a three-stage training framework reflecting the hierarchical nature of spatial intelligence.
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Dataset Collection
Dataset Unification
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Answer: [("bbox_2d'' [400, 81, 927, 890), "label":: "booksshelf")
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Figure 2: Overview of SpatialLadder-265 26k: dataset construction pipeline from raw data collection to question-answer pairs generation, with representative tasks including multi-view relative direction, single-image absolute distance, and corresponding grounding tasks.
3.1 DATASET CONSTRUCTION
Effective spatial reasoning requires diverse, high-quality training data spanning from basic percep- tion to complex reasoning. We introduce SpatialLadder-26k, comprising 26,610 samples across four complementary task categories that form a complete spatial learning curriculum. Figure illustrates construction pipeline and dataset composition.
Task Design and Hierarchy. Our strategically designed dataset comprises four task categories: object localization (5,929 samples), single-image spatial reasoning (5,929 samples), multi-view spatial reasoning (5,752 samples), and video spatial reasoning (9,000 samples). Object localization establishes perceptual foundations via precise bounding box predictions for spatially-referenced objects. Spatial reasoning tasks span three modalities and seven dimensions: relative direction, relative distance, absolute distance, object size, counting, room size, and appearance order. Single-image tasks provide the entry point for static scene reasoning. Multi-view tasks require cross-persper prosspperspeetive integration, synthesizing eight distinct viewpoints of identical environments. Video tasks incorporate temporal dynamics through 1-4 minute sequences 24 fps, demanding coherent spatiotemporal understanding. This hierarchical progression ensures systematic capability development from foundational perception complex spatiotemporal reasoning.
Construction Pipeline. Figure 2 details our standardized three-stage pipeline to ensure systematic data generation across all modalities. the first stage, we collect ScanNet's (Dai et al., 2017) comprehensive 3D scene reconstructions for object localization, single-image spatial reasoning. and multi-view spatial reasoning, and carefully sample 9,000 videos from SR-91k (Ouyang et al., 2025) to support video spatial reasoning. In the second stage, perform 3D-to-2D transformations and dataset unification, obtaining rich information including 3D bounding boxes, 2D bounding boxes, 3D absolute locations, 2D locations relative the camera, visibility ratios and object sizes. In the third stage, we generate diverse question -enswer pairs using templates adapted from VSI- Bench (Yang al., 2025a) to construct tasks across different spatial reasoning scenarios. Further details on dataset construction (e.g. quality assurance, QA templates) are provided in B.1.
3.2 TIREE-STAGE PROGRESSIVE TRAINING FRAMEWORK
Building upon SpatialLadder-266. design training framework that systematically constructs spatial intelligence through three progressive stages, as illustrated Figure 3, each addressing a specific level of the spatial reasoning hierarchy. The framework embodies the principle that robust
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Stagel: Spatial Perception Fine-tuning
Stage2: Spatial Understanding Fine-tuning
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Figure Three-stage progressive training framework SpatialLadder. Stage establishes perceptual grounding through object localization, Stage develops spatial understanding across seven dimensions using multimodal tasks, and Stage employs GRPO reinforcement learning with chain-of-thought generation to strengthen reasoning capabilities.
spatial reasoning emerges from the integration of perception, understanding, and reasoning, with each stage building upon foundations established in previous stages.
Stage 1: Perceptual Grounding through Localization. The first stage establishes foundational spatial perception via object localization 6k SpatialLadder-266 samples. The model learns to link visual inputs with spatial queries, producing JSON outputs containing object identities and 2D bounding boxes. This stage grounds abstract spatial concepts in concrete visual evidence. Through supervised fine-tuning, the model develops three core capabilities: distinguishing spatially relevant objects from background elements, robust detection tailored spatial reasoning contexts, and mappings between linguistic descriptions and visual regions. The training emphasizes localization precision, as accurate object detection underpins all subsequent spatial reasoning. By focusing exclusively on perceptual tasks, we ensure strong visual grounding before advancing to complex reasoning.
Stage 2: Spatial Understanding through Multi-dimeenional Tasks. The second stage broadens spatial comprehension by introducing comprehensive reasoning tasks that include size estimation, distance judgment, and orientation analysis across seven distinct spatial dimensions: relative direction, relative distance, absolute distance, object size, counting, room size, and appearance order. Training spans three modalities with distinct contributions: single-image tasks establish fundamental spatial relationships, multi-view tasks demand cross-perspectin integration and im- plicit 3D understanding, while video tasks add temporal dynamies and motion tracking capabilities. This multimodal approach creates robust spatial representations that generalize across visual contexts. The supervised fine-tuning requires flexible adaptation between multiple-choice questions testing discrete concepts and numerical questions demanding precise measurements, developing comprehensive spatial understanding that transcends individual task types.
Stage 3: Spatial Reasoning through Reinforcement Learning. The final stage transforms spatial understanding into explicit reasoning capabilities through reinforcement learning with chain- of-thought (Wei et al., 2022: Xu al., 2025) generation. We implement carefully designed reward structure that evaluates both reasoning quality and answer correctness:
R(o. y) Fromma (0) + Taccuracy (o, y)
Format rewards ensure structured reasoning by checking for proper <think> and <answer> tag usage, encouraging the model to explicitly articulate its reasoning process. Accuracy rewards are task-specific: binary for multiple-choiee questions and graduated for numerical answers based on relative error thresholds. This dual reward structure prevents the model from generating plausible- sounding but incorrect reasoning chains.
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We employ GRPO for stable policy optimization. For each question g. the model samples series of candidate answers {01, 02: ...,.) from the policy model Vold and optimizes the policy by maximizing the following objective function:
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EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Implementation Details. We implement SpatialLadder using Qwen2.5-VL-3B (Bai et al., 2025) the foundation model. The training procedure follows three-stage progressive schedule with stage-specific hyperparameter configurations. Stages and 2 employ supervised fine- tuning (Ouyang al., 2022), while Stage utilizes GRPO Guo et al. (2025) for reinforcement learning. Additional training details are provided in B.2.
Evaluation Benchmarks. We evaluate SpatialLadder Ladder six benchmarks across in-domain and out-of-domain settings For in-domain evaluation, we use VSI-Bench (Yang et al., 2025a) containing 5.155 video-based spatial reasoning questions and introduce two new benchmarks: SPBench- SI (1,009 single-image questions) and SPBench-MV (319 multi-view questions). Both SPBench benchmarks are constructed from ScanNet validation scenes using our pipeline, with strict scene- level separation ensuring zero overlap with training data. For out-of-domain evaluation, we assess generalization on CV-Bench (Tong al., 2024) for 2D/3D vision tasks, SPAR-Bench (Zhang al., 2025) for multi-difficulyy spatial reasoning, ViewSpatial-BB Beenh (Li et al., 2025a) for perspective- dependent spatial understanding, MMSI-Bench (Yang et al., 2025b) for diverse scenes spatial reasoning, and MindCube (Yin et al., 2025) for spatial mental modeling. Detailed benchmark and baseline descriptions are provided in Appendices C.I and C.2, respectively.
4.2 MAIN RESULTS
In-domain Performance. Table I presents comprehensive evaluation spatial reasoning bench- marks. SpatialLadder achieves state-of-the-art performance with 62.3% overall accuracy, surpassing all baselines including proprietary models. The performance gain is particularly pronounced on our proposed benchmarks: 70.2% on SPBench-SI (+29.9% over base model) and 70.9% on SPBench-MV (+34.3% over base model), demonstrating the effectiveness of our progressive training approach. Notably, while Spatial-MLLM achieves competitive performance on VSI-Bench (47.3%) using specialized 3D encoders, aoatiall-adde attains comparable results of 45.7% (+16.3% base model) using only the standard VLM architecture, validating that progressive training can substitute for architectural modifications. The consistent improvements across both numerical questions and multiple-choice questions indicate robust spatial understanding rather than task- specific overfitting. Further details of in-domain performance are presented in C.3.
Generalization Analysis. Table 2 demonstrates strong out-of-domain generalization with 45.0% overall accuracy, surpassing GPT-4o (42.7%) and achieving 6.9% average improvement over the base model. The gains are consistent across diverse evaluation settings, confirming the robustness of our learned representations. Notably, the model achieves a significant +8.6% improvement on ViewSpatial-Bench validating its proficiency in perspective-dependeer
spatial understanding and viewpoint transformation. Even more striking is the +10.2% gain on MindCube, which highlights the model's enhanced capacity for complex spatial mental modeling. Together with consistent improvements on CV-Bench (+3.1%). SPAR (+9.8%) and MMSI-Bench (+2.7%). these results demonstrate that progressive training fosters generalized spatial intelligence that transfers effectively to novel viewpoints and complex cognitive tasks. Further details of out-of- performance presented in C.4.

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Published as a conference paper at ICLR 2026
Table 1: Evaluation Results on In-domain Benchmarks. NQ and MCQ denotes numerical question and multiple-choice question, respectively. For each metric, bold numbers indicate the best performance, while underlined numbers represent the second- performance.
VSI-Bench
SPBeuch-SI
SPBench-MV
Model
Overall
NQ
MQQ
Avg.
MCQ
NQ
MCQ
Avg.
Proprictary Models
GPT-4o (lurs et 2024)
34.0
40.7
Semini-2.0-Flan (Team et 2024)
46.4
44.3
45.4
49.0
60.4
54.7
51,9
50.7
51.4
50.5
Open-Source Models
Lee.-VL-2.55-48 (Chen et al,, 2024)
30.6
34,1
32.4
31.8
53.3
42.5
37.7
51.4
43.2
39.4
Inter (Chen et 2024)
56.3
37.3
Kimi Bo-n-VL-A3B (Team el al., 2025)
2025)
31.8
25.5
28.7
25.7
44.9
35.3
23.3
57.6
37.0
33.7
LLuVA-Oneissoo--7
al., 2024a)
Qwem2,5-VL-78 Based Spatial Models
Owen2.5-VL-78 (Bai al., 20255
37.1
35.8
60.5
28.9
37.3
40.5
SpaceR-78 78 (Ouyang et 2025)
47.8
41.2
44.5
35.7
61.5
48.6
63.2
53.7
59.4
50.8
VILASR-7B (Wu al. 2025c)
47.4
43.4
45.4
63.7
56.2
57.6
51.1
Video-RI RI (Feng et al., 2025)
33.8
32.9
33.4
27.7
62.0
44.8
32.5
53.0
40.7
39.6
Qwen2.5.VV.. Based Spatial Models
Qwen2.5-VL (Bai al., 2025)
2025)
26.0
33.0
29.4
24.3
56.2
40.3
25.6
53.2
36.6
35.4
SpotialMLLLMBBB (Wu al., 2025a)
51.5
43.1
47.3
38.1
49.3
43.7
63.7
58.9
61.8
50.9
Spatiall adder-3B
50.8
40.5
45,7
58.6
81.8
70.2
68.2
75.0
70.9
62.3
Improvement
Table Evaluation Results on Out-of-domain Benchmarks. For each benchmark, bold numbers indicate the best performance, while underlined numbers represent the second- performance.
Model
CV-Bench
SPAR-Bench
ViewSpatial
MMSI-Bench
MindCube
Overall
GPT-4o GPT-lo et al., 2024)
75.4
36.4
32.6
30,3
38.8
42.7
I (Chen al., 2024)
74.4
30.6
37.9
26.3
18.3
Intem-LL-2.5-88 (Chen al., 2024)
36.3
43.2
25.7
18.7
LAVA--OneVision 7B (Li et al., 2024a)
58.3
31.2
27.5
24.5
47.3
37.8
Qwen2.5.
2025)
Qwen2.5 VL.3B 3B
2025)
26.5
Spatiallaader-33
73.7
34.4
44,2
29.2
43.4
45.0
Improvement
+3.1
+9.8
+8.6
+2.7
+10.2
+6.9
4.3 ABLATION STUDIES
Component Analysis. Figure reveals the critical interdependence of SpatialLadder's compo- nents. Stage 2 (spatial understanding fine-tuning) proves most essential, with its removal causing 9.4% accuracy drop, validating explicit spatial cognition as the training cornerstone. Stages 1 and 3 contribute meaningfully (1.8% and 2. 2.1% drops respectively), confirming progressive training's value. Excluding single-image and multi-view data causes most severe degradation (16.4% loss), affecting not only corresponding benchmarks but also video-based VSI-Bench performance. This demonstrates that multimodal diversity is fundamental for robust spatial reasoning across all modalities. Chain-of- reasoning provides consistent 0.8% gains, validating explicit reasoning in spatial tasks.
Training Dynamics. Figure demonstrates that the complete Spatiall-adder-3 consistently outperforms variants missing Stage 1 or Stage across accuracy reward curves. The reward standard deviation analysis reveals superior training stability for the full model, exhibiting the most significant variance reduction and smoothest convergence patterns. On VSI-Bench evaluation, the complete framework achieves highest accuracy while ablated variants show notable degradation, with Stage 2's absence producing the most pronounced performance decline. Appendix D.1 provides additional training dynamics with and without chain-of-thought.
4.4 IN-DEPTH ANALYSIS
Semantic Consistency Emerges through Reinforcement Optimization. We employ semantic entropy (Kuhn et al., 2023) quantify model uncertainty. As shown in Figure during Stages 1-2 where the model establishes perceptual foundations and spatial understanding capabilities, entropy increases from 1.24 to 1.47 spatial capabilities transcend initial misconceptions and expand the

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500
1000
1500
2000
2500
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300
1000
1500
2000
2500
500
1000
1500
2003
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Figure 4: Impact of progressive training stages. Left: accuracy rewards over training steps; Middle: reward standard deviation training steps; Right: VSI-Bench performance comparison.
Performanet Difference
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Figure 5: Ablation study results.
Figure 6: Semantic entropy dynamics.
exploration space for improved reasoning. Subsequently, during Stage 3 reinforcement learning, entropy steadily declines from 1.47 to 0.66, marking transition from broad exploration focused reasoning convergence. This quantitative progression validates our three-stage strategy: establishing comprehensive foundations, expanding the reasoning space, and achieving robust spatial intelligence through convergence. Further details provided in Appendix C.6.
Visual Attention Becomes Precisely Object-centric through Progressive Training. To derstand how our training framework influences internal mechanisms, we analyzed the visual attention patterns of SpatialLadder and Qwen2.5-VL-3B. Qualitatively, Figure (top) reveals that SpadialLadder exhibits significantly more concentrated attention on task-relevant objects. Crucially, in relational tasks, the model generates distinct, simultaneous attention hotspots for all involved entities, whereas the base model typically exhibits diffuse or singular attention patterns.
We conducted quantitative evaluation of attention distributions using 400 samples from SPBench- SI, with two metrics: Visual Attention IoU, which measures the concentration of attention within object bounding boxes, and Visual Attention Entropy, which quantifies the degree of attention dispersion across the visual field. As shown in Figure 7 (bottom), Spatiall.aderr achieves superior overall performance with 73.5% accuracy and 37.7% visual attention IoU compared to the base model's s 32.1 % accuracy and 33.8% IoU. Additionally, SpatialLadder exhibits lower visual attention entropy (0.176 vs. 0.1)3), confirming that our progressive training effectively reshapes the model's 's mechanism to precisely focus relevant targets during spatial reasoning. Notably, this phenomenon is consistent across all four task lypes-spanning both single-object and multi-object scenarios- SpatialLadder consistently demonstrates higher accuracy aligned with superior attention concentration. Further details are provided in Appendix C.7.
Hierarchical Reasoning Structures Develop Naturally from Perceptual Foundations. Qualita- tive analysis indicates that SpatialLadder acquires systematic spatial cognition through foundational perceptual training. As illustrated in Figure 8, the model exhibits a hierarchical cognitive architecture in which accurate identification spatial elements serves as the perceptual basis for constructing coherent logical reasoning chains via structured analysis. Beyond basic reasoning, the model demonstrates advanced metacognitive capabilities, including selffverification and error correction, which help maintain consistency throughout the reasoning process.
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Published as a conference paper at ICLR 2026
Object Size Estimationn (Single Object)
Relative Direction (Nuitiple Objects)
Backpack
Toblee
Qw1.2.-A11.an
Atteation Varbetian
Allention Varietion
Object Size
Absolute Distance
Relative Direction
Beiathve Distance
Owee
Oweed
2.22
323
1.29
038
43-
814
034
6:44
314
20
13-
032
0.22
Figure 7: Visual attention comparison between SpatialLadder and Qwen2.5-VL-3B, Top: Representative attention distribution patterns for both models. Bottom: Quantitative analysis of performance accuracy, attention loU, and attention entropy metrics.
Relative Distance
Route Plan
Messuring from clesest point ohject, [laptop, competer mouse, teleplione, door] is the closest to the keyboard? A. Laptop E. computer mouse C. télehhnn D. doer
objects
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which
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bathroom
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the sials,
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atteation the bathiuh. aliges the provided, let's by checking visually.
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likely
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c
Observation
Reasoning
Reflection
Conclusion
Figure 8: Hierarchical reasoning process demonstration in SpatialLadder.
In relative distance tasks, SpatialLadder explicitly decomposes spatial relationships into inter- pretable subcomponents, while in path-planning scenarios it systematically evaluates complex spatial layouts and navigational constraints. This structured chain-of-thought behavior highlights how robust perceptual grounding naturally scaffolds higher-order spatial reasoning.
Importantly, SpatialLadder consistently produces correct conclusions accompanied by clear and well-organized reasoning steps. These results suggest that strengthening foundational spatial per- ception effectively supports hierarchical reasoning, thereby validating the design and effectiveness of our training framework.
Reinforcement Learning Unlocks Performance Beyond the Limits of Supervised Training. To isolate the unique contribution of Stage3, we perform controlled comparison between extended supervised fine-tuning and our RL stage. Using the standard Stage pipeline as the baseline, we investigate whether adding extra epoch of Stage-2 SFT can replicate the gains brought by RI.

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Published as a conference paper at ICLR 2026
Table Performance comparison between extended SFT and RL. RL surpasses the performance limits of SFT, while extended SFT leads to performance degradation.
Model
VSI-Bench
SPBench-SI
SPRench-MV
Overall
Swen2.5-VL-33B (Backbone)
29.4
40.3
36,6
38.8
Stage 1-2 SFT Training
43.9
68.5
69.9
60.2
Stage 1-2 (2 Epocks) SFT Training
43.4
64.3
67.0
58.2 (L 2.0)
Stage 1-3 Training (Ours)
45.7
70.2
70.9
62.3 (1 2.1)
As shown in Table 3, when trained on the same dataset for the same number of additional epochs, the model with extended SFT consistently underperforms the baseline across all benchmarks, resulting an overall performance drop of 2.0%. This degradation suggests that prolonged supervised training leads to overfitting and negatively impacts generalization.
In contrast, incorporating Stage3 (RL.) effectively avoids this performance collapse and further surpasses the SFT performance ceiling. The RL-enhanced model achieves consistent improvements across all benchmarks, yielding an overall gain of 2.1%. These results demonstrate that reinforce- ment learning plays critical role in enhancing spatial reasoning capability, enabling performance improvements that cannot be achieved through purely supervised training alone.
Well Preservation of General Multimodal Capa- bilities. Since SpatialLadder-26k consists exclu- sively of spatial perception and spatial reasoning tasks, natural concern is whether our three-stage training pipeline induces catastrophic forgetting of general multimodal capabilities. To assess this risk, we evaluate the model on two widely used general- purpose multimodal benchmarks: MMBench(Liu et al., 2024), which measures broad vision-language understanding, and MMMU(Yue et al., 2024), which focuses multidisciplinary visual reasoning.
Table 4: Results on general benchmarks
Method
MMBench
MMMU
Qwen2.5-
83.3
48.3
SpatialLadder-BB
82.4
47.1
-0.9
-1.2
As reported Table 4, SpatialL shows only marginal performance reductions compared to the base model, with drops of 0.8% on MMBench and 1.2% MMMU, Given the substantial gains achieved on spatial reasoning benchmarks, these minor degradations indicate that our progressive training strategy significantly enhances spatial intelligence while largely preserving the model's general multimodal competence.
Additional analysis about comparison with other spatial reasoning dataset, dataset scaling and progressive training order are provided in D.2, D.3 and D.4 respectively.
5 CONCLUSION
This work addresses the perception-reasoning gap in VLMs for spatial tasks through a systematic solution. We introduce Spatialladder-266;; multimodal dataset covering object localization as well as single-view, multi-view, and video-based spatial reasoning, and propose a three-stage progressive training framework that develops spatial intelligence from perception to understanding and reasoning. Built upon this framework, our Spatiall Ladder model achieves state-of-the-an performance across multiple benchmarks, demonstrating strong generalization both in-domain and out-of-domain. Extensive ablation studies further validate the effectiveness of each component. Together, these results establish new paradigm for spatial reasoning in VLMs and open up promising directions for future research, discussed in Appendix E.
ACKNOWLEDCEMENTS
This work was supported by National Natural Science Foundation of China (No. 62506332) and the Key Research and Development Program of Zhejiang Province, China (No. 2024C03255).
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ETHICS STATEMENT
This work does not involve human subjects, personal data, or sensitive information. All datasets used in our experiments (VSI-Bench, SPBench-SI, SPBench-MV, CV-Bench, SPAR-Bench, ViewSppttil-- Bench) are publicly available benchmark datasets designed for evaluating visual spatial reasoning in VLMs. We strictly adhered ethical research practices and did not conduct any data collection that could raise privacy, security, or faimess concerns. Our methods Sathhodd-SpatiaLL... SpatialLadder and progressive three-stage training fradework-address the perception-reasoning gap VLMS for spatial tasks, developing robust spatial reasoning capabilities without introducing risks of harmful applications. To the best of our knowledge, this research complies with the ICLR Code of Ethics and poses foresecable ethical concerns.
dataset
REPRODUCIBILITY STATEMENT
We have made extensive efforts to ensure the reproducibility of our work. Comprehensive details of dataset construction are provided in B.I, while training configurations and hyperparameters systematically reported in B.2. Detailed dataset descriptions are documented in C.I. The comprehensive evaluation results are outlined in C.3 and C.4, and the implementation details of analysis experiments are thoroughly described in C.6 and C.7. Upon acceptance, we will release our models, together with training and evaluation code, to facilitate replication and further research.
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TECHNICAL APPENDICES AND SUPPLEMENTARY MATERIAL
A PRELIMINARY ANALYSIS
To validate our hypothesis that spatial reasoning failures stem from inadequate perceptual grounding rather than reasoning incapacity, conducted controlled experiments examining how progressive perceptual hints affect model performance. We constructed a diagnostic dataset of 200 spatial orientation tasks from ScanNet validation scenes, each requiring determination of relative positions between object pairs from the camera's perspective.
We evaluated Qwen2.5-VL-3B (Bai al., 2025) under three conditions: (1) baseline with raw images and questions,
(2) location hints adding colored bounding boxes around target objects, and (3) full hints incorporating directional arrows within bounding boxes. Results demonstrate monotonic improvement with enhanced perceptual grounding: baseline accuracy of 36.5% improves 41.5% with location hints (+5.0%) and 46.0% with directional hints (+4.5% additional).
These findings directly motivate our progressive training approach. This experimenta evidence establishes that robust spatial reasoning cannot be achieved through end-to-end learning but requires systematic construction from perceptual foundations to abstract reasoning. After training, our model SpatialLadderr achieves consistently high performance across all conditions: 82.0% without hints, 82.5% with location hints, and 83.5% with full hints. This minimal variation (1.5% range) demonstrates that progressive training successfully internalizes spatial perception capabilities, eliminating dependence external scaffolding.
B ADDITIONAL METHOD DETAILS
B.1 DETAILS OF SPATIALLADDERR 26A CONSTRUCTION
Table 5: Question templates for tasks in SpatialLadder-200 26k.
Task
Question Template
Object Counting
How many {category (s) appear?
Absolute Distance
Measuring from the closest point of each object, what is the distance between the {object 1) and the [object2} (in meters)?
Object Size
What the length the longest dimension (length, width, or height) of the {ohject). measured in centimeters?
Relative Distance
Measuring from the closest point of each object, which of these two objects (fchoice a}. {choice b}} is closer the {category)?
The question template relative direction tasks varies between single-image and multi-view modalities.
Single-image:
From the camera's 's perspective, is the {object 1} to the {object 2} {choice a}. {choice b}. {choice c} or {choice d??
Relative Direction
Multi-view: If am standing by the {positioning object) and facing the (orienting object), the {querying object} my {choice a}. {choice b {choice e} {choice d}? ? The directions refer to the quadrants of a Cartesian plane (if I am standing at the origin and facing along the positive y-axis).?
Object Localization
{question} Please carefully observe the image first identify the object(s) referred to in the question. Note that each object type appears only once in the image. Then provide the 2D bounding box coordinates and labels of the related objects in JSON format.
Question-answer Generation Details Based on the metadata unified from ScanNet, we further construct question-answer pairs for various single-image and multi-view spatial reasoning tasks. The generation process is described as follows:
Object Counting: This task involves a single object category and designed exclusively for the multi-view setting. We first identify the target object category, then determine the
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number of distinct instances that appear across all views by checking their presence in each camera view, and finally use the aggregated count as the answer.
This task involves two objects. Using their 3D locations from the metadata, we calculate the Euclidean distance between them as the answer. To ensure clarity and avoid ambiguity, we enforce that the computed distance must exceed the minimum size of the two objects.
Absolute Distance:
Object Size: This task focuses on a single object. We estimate the object's size using the maximum dimension of its 3D bounding box, while filtering out objects that are either excessively large or small to ensure human-scale spatial reasoning.
This task involves three objects-a target object and two candidate objects. We compare the distances from the target object to each candidate and select the closer one as the answer. To prevent ambiguous cases, we require the larger distance to be at least twice the smaller one.
Relative Distance:
The construction of this task differs between single-image and multi- view modalities. For the single-image setting, the task involves two objects, and their relative orientation is defined with respect to the camera viewpoint. Specifically, we compute the left/right relation based on their 2D locations relative the image plane, and the front/back relation based on their depth from the camera. Composite relations (e.g., left-front, left-back, right-front, right-back) are included when applicable. For the multi- view setting, the task involves three objects: positioning object, an orienting object, and a querying object. We define vectors from the positioning object to the orienting object (α) and to the querying object (b), and compute the angle 0 between them using cos (f)
Relative Direction:
a-E
|
The relative direction of the querying object is then determined according to this angular relationship.
The question templates employed for generating QA pairs across various spatial reasoning and object localization tasks in SpatialLadder-266 are detailed in Table
Quality Assurance. To ensure dataset reliability, we implement multiple filtering mechanisms. Scene diversity is maintained by limiting questions per scene to prevent overfitting to specific environments. Object diversity is enforced by restricting the number of samples constructed from the same object type within a scene, avoiding bias toward particular categories and ensuring question variety. Noisy objects (e.g. wall, floor and ceiling) are filtered focus human-scale spatial reasoning. Minimum visibility threshold (40%) ensures that spatial judgments are based on sufficient visual evidence. Objects must be uniquely identifiable within their context to avoid ambiguity. These constraints eliminate approximately 90% of initially generated samples, resulting high-quality dataset where each sample provides clear spatial learning signals.
Table 6: Detailed statistics of the spatial reasoning subset in Spatiall Laddialladdde-26k..
Numerical Question
Multiple-choice Question
Modality
Total
Obj Cnt.
Abs. Dist.
Obj Size
Room Size
Rel. Dist.
Rel. Dir.
Appr. Order
Single-Image
1.127
1,514
1,034
2,253
$,929
Multi- View
217
817
1,867
635
2,162
5,752
Video
507
1,500
1,331
150
1.134
3,061
1.317
9,000
The Statics of SpatialLadder-266 The distribution of spatial reasoning tasks across different modalities in our constructed SpatialLadder-266. is presented in Table 6. In SpatialLadder-266. object localization tasks are based on the single-image modality but remain independent of specific spatial reasoning task types. Each single-image spatial reasoning task is paired with corresponding object localization task.
B.2 DETAILS OF TRAINING IMPLEMENTATION
Prompt Used for Training. The system prompt and user prompt employed in the SpatialLadder three-stage training framework are presented in the boxes below. The post prompt design in Stage and Stage 3 varies across task types: for multiple-choice questions, the prompt guides the model to
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output the corresponding option, whereas for numerical questions, the prompt instructs the model to provide a numerical answer.
Prompt for Stage
System Prompt: "You helpful assistant."
User Prompt: {question) + "Please carefully observe the image first identify the object(s) referred in question. Note that each ohject type appears only once the image. Then provide the hounding box coordinates and labels of the related objects in JSON format."
Prompt for Stage 2
System Prompt:
"You a helpful assistant."
User Prompt: {question) + Post qrestii"question type")
Post Prompt:
Multiple-choice Question: "Please answer with the option's letter from the given choices (e.g.. g., A,
B, etc.) directly."
Numerical Question: "Please answer the question using numerical value (e.g., 42 or 3. directly."
Prompt for Stage 3
System Prompt:
"You are a helpful assistant." User Prompt: {question} } "Please think about this question as if you were a human pondering deeply. Enguge in internal dialogue using expressions such as 'let think', 'wait', 'Hmm', 'oh, I see', 'let's break it down', or other natural language thought expressions. It's encouraged include self-reflection or verification the reasoning process." Post Prompt:
Prompl["question type"]
Multiple-choice Question: "Please provide your detailed reasoming between the <think> </think> lags, and answer the question with option's letter from given choices (e.g.
A, B, etc.) within the <answer> < </answer> tags.""
Numerical Question: "Please provide your detailed reasoning between the <think> </think> tags, and then answer question with numerical value (e.g. 42 3.1) within the <answer> </answer> tags."
Table 7: Hyperparameter used in Stage 1-2.
Table 8: Hyperparameter used in Stage 3.
Hyperparameter
Value
Hyperparameter
Value
per_device_tre .traiettann....eeeeee....e.e aacii_batc__sst
num generattisi
8
per device arevice.train... wwateettttaaaaaaah..e.eeea
batch
2
bfl6 data_seed gradient checkpointing
true
madient.accummultin stops bf16 data seed
4
42
true
42
attn implementation
attention 2 cosine 0.1
gradient checkpointing attn implementation num_train_epoche max-pixels min_pixels
true
Ir scheduler type warmup ratio num-train_epoch. max_pixels min pixels
flash attention 2
100.352
100,352
12,544
12,544
0.01
Reproduction details. Our model was trained on NVIDIA A6000 GPU cluster with
48GB memory per device. The training process consisted of three distinct stages: stages 1-2 employed supervised fine-tuning methodology implemented via the HuggingFace Transformers Reinforcement Learning (TRL) framework, with corresponding hyperparameters detailed in Table 7. Stage 3 utilized GRPO reinforcement learning, implemented through the VLM-RI framework (Shen et al., 2025), with corresponding hyperparameters specified in Table 8.
Details of Stage and Stage 2. In our progressive three-stage training framework, Stage develops spatial perception capabilities through targeted spatial localization tasks, while Stage
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enhances spatial understanding through multi-dimensional spatial reasoning tasks. Both stages employ supervised fine-tuning methodology, optimizing the standard cross-entropy loss function:
(0) = Σ log P (0(0) 012-11 (000))))))) (0.00 .9.0)
where represents the input visual information, 4 denotes the textual query and instruction, o( represents the i-th token in the generated response, and ((1:2-1) corresponds to the preceding context tokens. This supervised learning approach establishes the foundational spatial capabilities that are subsequently refined through reinforcement learning in Stage 3.
Details of Cold Start. Before the formal GRPO training in Stage 3, we perform cold-start (Guo et al., 2025) phase to ensure that the model can more reliably generate outputs that satisfy the required format. Specifically, thought augmented data with composite formatting constraints. Concretely, based on the spatial reasoning tasks from SpatialLaddeer26f: question-ance answer pairs with reasoning chains. The generated responses are then filtered using a reward function under requirements, and (2) its accuracy reward must exceed a predefined threshold. The resulting seatiidn-sapped dataset is defined as:
adopt rejection sampling strategy to construct chain-of-
use the Qwen2.5-VL-7B model to generate candidate
criteria: (I) the response must strictly satisfy the predefined formatting
Deoldstart = {(vi, Qi 0z) (vi, Os, M/) E D randicate A R(os, Vi) 1 1
(4)
where D Deandicate denotes the of candidate question-aie answer pairs generated by Qwen2.5-VL-7B on SddrallL adder-26k, et represents the visual input of the i-th question, d denotes the question text, or corresponds the model's response for the i-th question, and is the accuracy reward threshold. This process yielded total of 1,255 cold-start training samples.
Details of Reward Function Stage introduces the GRPO reinforcement learning algorithm to further stimulate the model's spatial reasoning capabilities through carefully designed reward mechanisms. Our reward system includes format rewards and accuracy rewards.
The format reward ensures structured model outputs by requiring the model to place its reasoning process and final answer within <think> </think> and <answer> ... </answer> tags, respectively:
[1,
if matches format
TFommat (0)
00 otherwise
The accuracy reward employs differentiated evaluation strategies based question types. For multiple-choice questions, we adopt strict exact matching criterion:
Time (o, v) I[(o y)
where o represents the model's prediction and denotes the ground truth label for the question.
For numerical answer questions, considering the continuous nature of numerical predictions, design a weighted relative accuracy measure based on confidence intervals:
Foum (o, y) =
Σ
0 v)
<
(6)
T
y
TET
where T = [0.50, 0.55, 0.95) represents a series of confidence thresholds.
The unified accuracy reward function defined as:
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Tmme (m y), (num (0, v), if E NQ
q E MCQ
Taccuracy (0, v)
where represents the input question, MCQ denotes the set of multiple-choice questions, NQ denotes the set numerical answer questions.
The final reward function integrates both format and accuracy dimensions:
R(o y) Tormat (o) + Faocuray (o, v)
(8)
C ADDITIONAL EXPERIMENTS DETAILS
Table 9: Detailed statistics of the SPBench-SI and SPBench-MV.
Numerical Question
Multiple-choice Question
Benchmark
Total
Obj. Cnt.
Abs. Dist.
Obj. Size
Rel. Dist.
Rel. Dir.
SPBench-SI
149
463
91
306
1,009
SPBench-MV
70
30
158
17
44
319
C.I
DETAILS OF BENCHMARKS
VSI-Bench (Yang al., 2025a): VSI-Bench is comprehensive evaluation benchmark for assessing visual-spatial intelligence in Multimodal Large Language Models (MLLMs) through egocentric video understanding. The benchmark comprises over 5,000 question- answer pairs from 288 real-world videos sourced from ScanNet Dai et al. (2017), ScanNet++ Yeshwanth al. (2023), and ARKitScenes (Baruch al., 2021), spanning diverse environments across multiple geographic regions.
SPBench-SI & SPBench-MV: SPBench-SI and SPBench-MV are evaluation benchmarks constructed using the SpatiallLadder-26k pipeline applied to the ScanNet validation set. SPBench-SI serves as single-image evaluation benchmark designed to assess models' spatial understanding and reasoning capabilities from individual viewpoints, encompassing four task categories: absolute distance, object size, relative distance, and relative direction, with a total of 1,009 samples. SPBench-MV constitutes a multi- view evaluation benchmark that requires models to perform joint spatial modeling across multiple viewpoints. SPBench-MY additionally incorporates object counting tasks to evaluate models' capabilities in identifying and enumerating objects within multi-view scenarios. Both benchmarks undergo rigorous quality control through the standard pipeline filtering strategies supplemented by manual curation to ensure data disambiguation and high-quality annotations. The detailed statistics of SPBench-SI and SPBench-MV are provided in Table
CV-Bench (Tong et al., 2024): CV-Bench addresses limitations of existing ceeitnccentric benchmarks through 2,638 manually-inspecte examples. The benchmark repurposes established vision datasets -Deetkk (Zhou al., 2017), COCO (Lin et al., 2014), and OMNI3D (Brazil et al., 2023) evaluate MILMs on fundamental computer vision tasks. The evaluation encompasses 2D spatial comprehension through spatial relationships and object counting, while 3D understanding is assessed via depth ordering and relative distance estimation.
SPAR-Bench (Zhang et al., 2025): SPAR-Bench constitutes a comprehensive evaluation framework for systematically assessing spatial perception and reasoning capabilities in VLMs. The benchmark encompasses 20 diverse spatial understanding tasks spanning single-view. multi-view, and temporal video modalities, incorporating 7,207 manually verified question-answer pairs to ensure annotation quality and reliability.
ViewSpatial-Best (Li et al., 2025a): ViewSpatial-Bers -Bnnch is a comprehensive evaluation framework comprising over 5,700 question-answer pairs across 1 1,000+ 3D) scenes from
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ScanNet (Dai al., 2017) and MS-COCO (Lin et al., 2014) validation datasets. This benchmark evaluates VLMs' spatial localization capabilities from both egocentric and allocentric viewpoints, addressing the critical gap in perspective-taking abilities essential for embodied (Wang et al., 2025b) interaction and multi-agent collaboration.
MMSI--ench (Yang et al., 2025b): MMSI-Bench focuses on multi-image spatial intelligence for MLLMs. The benchmark contains 1,000 manually-curatie multiple- choice questions derived from over 120,000 images, each paired with carefully designed distractors and stepwise reasoning process. MMSI-Bench evaluates models spatial reasoning skills, including grounding, overlap matching, scene reconstruction, situation transformation, and spatial logic.
MindCube (Yin et al., 2025): MindCube is a benchmark for evaluating spatial reasoning in VLMs from limited visual inputs. It contains 15,1 questions across 3,268 images, testing capabilities such cognitive mapping, perspective-tukee MindCube is designed to identify gaps in current VLMs' spatial understanding and support research on structured spatial representations.
and mental simulation.
C.2 DETAILS OF BASELINES
GPT-4o (Hurst et al., 2024): GPT-4o is a multilingual and multimodal generative transformer released in May 2024, supporting text, image, and audio understanding and generation with strong general capabilities.
Gemini-2.0-Flaste (Team et al., 2024): Gemini 2.0 Flash is multimodal model optimized for agent-centric applications, featuring efficient computation, integrated tool use, multi- modal generation, and a MM-token context window with improved quality over previous Flash versions.
LL2.54BL-2.5-44/88B (Chen et al., 2024): Intern 2.5 an enhanced version of InternVL 2.0 with improved training strategies and data quality, achieving competitive performance across reasoning, document understanding, and video comprehension.
Kimi-VL-A3I (Team et al., 2025): Kimi-VL-A3P is efficient MoE-based VLM (activating 2.8B parameters) with strong multimodal reasoning, long-context processing, and agent capabilities.
LLaVA-One V (Li et al., 2024a): -LaVA-OneVision-77 an open MLLM performing well on single-image, multi-image, and video tasks, showing strong cross- modal transfer and particularly effective video understanding.
Qwen2.5-VL-3B77 (Bai al., 2025): Qwen2.5-VL. is VLM with enhanced recognition, localization, document parsing, and long-video comprehension, supported by dynamic resolution handling for variable-sized inputs.
SpaceR-7B (Ouyang al., 2025): SpaceR-7B is video spatial reasoning model trained with reinforcement learning using verifiable rewards. It incorporates map imagination mechanism to infer spatial layouts during reasoning and demonstrates strong performance on VSI-Bench, surpassing GPT-4o.
VILASR-7B (Wu et al., 2025c): VILASR- introduces a "drawing-to-reason" to-reason" paradigm, enabling the model to perform spatial reasoning through elementary drawing operations. It uses a three-stage training pipeline (synthetic cold-start, reflective rejection sampling, and reinforcement learning) learn structured spatial reasoning and achieves strong results spatial benchmarks.
Video-R1 (Feng et al., 2025): Video-RI extends the R.I reasoning paradigm to video understanding, leveraging the T-GRPO algorithm to better capture temporal dynamics. The model is trained on both image and video reasoning tasks and demonstrates robust spatial- temporal reasoning, surpassing GPT-4o on VSI-Bench while performing well general video benchmarks.
Spatial-MLLMM44B (Wu et al., 2025a): Spatial-MLLM
dual-encoder spatial rea- soning framework, combining pretrained 2D visual encoder for semantic understanding with a spatial encoder for 3D structure reasoning. Extensive experiments show state-of- the-art performance across various visual spatial understanding and reasoning tasks.
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Table 10: Evaluation results on VSI-Bench. For each metric, bold numbers indicate the best performance, while underlined numbers represent the second-best performance.
Numerical Question
Multiple-choloe Question
Model
Obj
Abs. Dist.
Obj. Size
Room Size
Rel. Dist.
Rel. Dir.
Route Plan
Appr. Order
Proprictary Models
GPT-4o
46.2
5.3
43.8
38.2
37.0
41.3
31.5
28.5
34,0
Gomini-2.0-Flash
30.9
31.8
51.3
46.3
24.5
55.1
45.4
Open-Source Models
Inter VL-2..L-2..5444
LatemVL-22.588
50.6
31.3
40.2
39.3
45.1
41.4
29.4
43.9
40.2
Kimi-VL-A3B
30.4
42.1
26.3
11.2
28.7
LLaVA-One Vision-78
46.1
26.2
36.3
29.5
30.8
37.2
35.
21.8
33.1
0wen2.5.VI-22 Spotial
Owen2.5.VL. VL-7B
43.5
15.1
48.5
41.1
36.3
40.1
28.4
33.7
35.8
SpaceR-7B
VILASR-7B
63.5
34.4
60.6
30.9
48.9
45.2
30.4
49.2
45.5
Video-RI
34.0
23.0
41.6
36.7
36.8
34.7
31.4
28.8
33.4
Qwen2.5.Vi. VL.33 Raxed Spatial Models
Owen2.5 LW-32.5-VL-3B
32.9
28.5
Spatial-MLLM-18
65.6
35.5
64.2
40.5
41.3
47.9
34.0
49.2
47,3
ap 63.5
34.3
61.7
36.4
45.7
Improvement +30.6
+12.2
+44.4
+12.44
+12.6
+0.6
+9.3
47.9
+16.3
Table 11: Evaluation results on SPBench-SI. For each metric, bold numbers indicate the best performance, while underlined numbers represent the second-best performance.
Numerical Question
Multiple-choice Question
Model
Avg.
Abs. Dist.
Obj. Size
Rel. Dist
Rel. Dir.
Proprictary Models
GPT-4o
Gemini-2.0-PB 0.0Faahh
33.1
64.9
81.3
39.5
54.7
Open-Source Models
InternVL-2.55 L--2.5-BB
27.3
36.2
73.6
33.0
42.5
InternVL-2.5--8
40.8
35.6
42.3
Kimi-VI VL.BABBBB
11.3
40.2
62.6
27.1
35.3
LLaVA-One Vision-7B
27.2
54.9
27.1
33.2
Qwen2.5-VLL77 Based Spotial Models
V1..77
27.7
45.0
83.5
37.6
48.4
SpaceR-7B
8,4
62.9
80.2
42.8
48.6
VII-AS--7B
10.3
63.0
81.3
46.1
50.2
Video R1
5.1
50.3
82.4
41.5
44,8
(wen2.5. VL-38 VLL-33 Baxed Spatial Models
SpatialMLLL. Spatiall.adder.33
16.4
59.7
69.2
29.4
43.7
Improvement
+14.6
+53.9
+5.5
+45.8
+29.9
Table 12: Evaluation results on SPBench-MV. For each metric, bold numbers indicate the best performance, while underlined numbers represent the second-best performance.
Numerical Question
Multiple-chokee Question
Avg.
Obj. Cni
Abs. Dist.
Obj. Size
Rel. Dist.
Rel. Dir.
Pruprictary Models
GPT-40
66.3
12.0
43.8
82.4
36.4
48.2
Semini-220--laas
40.7
76.5
51.4
Open-Source Models
MMeemVL-22.5..1
65.1
24.0
23.9
82.4
20.5
43.2
V125581
50.0
25.0
37.0
88.2
6.8
41.4
Kimi VL-A3B
13.7
23.3
33.0
76.5
38.6
37.0
LLaVA-OOnVVssion-
21.1
21.3
19.2
76.5
22.7
32.2
Owen2.5. WVV-7 Based Spatial Models
46.1
11.0
35.4
88.2
37.3
SpaceR-7B
90.1
33.7
65.1
82.4
25.0
59.4
VILASR-7B
65.3
34.7
68.7
88.2
29.5
61.8
Video-RI
33.9
18.0
45.6
76.5
29.5
40.7
Owen2.5. VVL-3 Rased Spatial
Owen2.5 2.omm2555LL3.1 VL-3B
36.7
14.7
14.9
88.2
18.2
36.6
Spattal-MLLM-4B
88.2
SputiallLakder-31
94.9
34.7
76.4
100
50.0
71.2
Improvement
+20.0
+34.6
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C.3 DETAILS OF IN-DOMAIN BENCHMARKS RESULTS
addeer We present the detailed evaluation results of VSI-Bench in Table 10. Our proposed Spatiall adder achieves overall accuracy of 45.7%, surpassing all compared models except Spatial-MLLLM, including those with 2-3 times larger parameter sizes. On average, SpatialLadder improves performance by 16.3% and demonstrates consistent gains across all sub-tasks of VSI-Bench. Notably, while Spatial-MLLAN leverages an additional 3D encoder, our SpatialLadder relies solely on the vision encoder of Qwen2.5-VL-3B
Furthermore, we report the detailed results of SPBench-SI and SPBench-MV in Tables 11 and 12, respectively. Spatiall attains 70.2% and 71. 71.2% accuracy on these two benchmarks, corresponding to relative improvements of 29.9% and 34.6% over the base model Qwen2.5-VL. 3B, and consistently outperforms all compared baselines.
Table 13: Evaluation results on CV-Bench. For each metric, bold numbers indicate the best performance, while underlined numbers represent the second-best performance.
2D
3D
Model
Overall
ADE20K
COCO
Avg.
Omni3D
GPT-4o
65.1
73.8
69.4
81.3
75.4
IntermVL-2.5-4B
68.6
78.5
73.5
75.1
74.4
Kimi-VL-A3B
41.9
42.4
41.9
54.7
48.3
I.L.V OneVision-78
50.6
55.8
53.2
63.5
58.3
Qwen2,5-VL-7B
69.5
80.5
75.0
83.1
79.0
Qwen2.5-VI-33
63.2
75.0
69.1
72.2
70.6
SpatialLadder-3B
67.1
74.9
73.7
Improvement
+3.9
+2.6
+3.3
+2.7
+3.1
Table 14: Evaluation results on ViewSpatial- For each metric, bold numbers indicate the best performance, while underlined numbers represent the second-best performance.
Camera Perspective
Person Perspective
Model
Overall
Rel. Dir.
Ohj. Ori.
Avg.
Obj. Ori.
Rel. Dir.
Sce. Sim.
Avg.
GPT-4o
41.5
19.6
33.7
41.2
32.8
21.9
31.5
32.6
IntemnV1.22.5544
37.1
31.8
40.8
43.6
37.1
26.1
35.1
37.9
Kini-VL-A3B
22.1
25.1
43.9
20.3
LLaVA LaaVA-OneVil Vision-BB
29.8
26.1
28.5
22.4
31.0
26.9
26.5
27.5
Qwen2,5-VL-7B
47.8
30.9
41.8
41.6
35.4
26.9
39.8
37.9
Qwen2.5 VLL-3.5-VL-3BB VL-3B
43.5
32.5
39.5
40.0
29.9
26.3
32.0
35.6
Spatiall adder-38
24.1
39.6
71.1
34.4
38.9
48.5
44,2
Improvement
+31.1
+12.6
+16.5
C.4 DETAILS OF OUT-OF-DOMAIN BENCHMARKS RESULTS
We present the detailed evaluation results on CV-Bench Table 13. Our proposed Spatiall adder adder achieves overall performance of 73.7%, 5.3% below the best-performing baseline model. Nevertheless, surpasses the base model Qwen2.5-VL-3B by 3.1%, demonstrating the robustness of our training framework in enhancing model performance. The detailed results on ViewSpatial- Bench are provided Table 14, where SpatialLadder achieves an overall accuracy outperforming all compared models and surpassing the base model by 8.6%. The detailed evaluation results on the out-of-domain benchmark SPAR-Bench are provided in Table in the main text.
44.2%,
C.5
SFT TRAINING STABILITY
We show in Figure the training dynamics of Stage 1-2 SFT, including loss and mean token accuracy. The results indicate that the model trains smoothly and converges rapidly in both stages, demonstrating the high quality and consistency of SpatialLadder 26k and ensuring stable performance during full-model fine-tuning.
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Accuracy Stage
Stage
Stage
Accuracy Stage
10
Ancurway
LOSS
LAoS
85-
1:4
120 Stags
12.
-20
246
110
5001
110
230
401
Stagu
Staps
Slaps
Figure 9: Training dynamics of loss and mean token accuracy during stage 1-2
C.6 DETAILS OF SEMANTIC ENTROPY.
To quantify response diversity for uncertainty analysis, we introduce semantic entropy as clustering-based metric. For each question q. sample responses {0r, .2...., oG} } at temperature 0.9 (8 samples per question) and partition them into semantic clusters {C1, C22 ...; Ck based on accuracy rewards, where C {o; R(o;) = r for distinct reward values R (T1. ... TK Semantic entropy is then computed as:
K
|c||
(B(g)
g))
=
Σ ==1
pi log where pt = N
This measure captures the distributional diversity of semantically distinct response clusters, provid- ing principled approach to quantify model uncertainty beyond surface-level textual variations.
C.7 DETAILS ATTENTION ANALYSIS.
For visual attention analysis, we use two metrics, Visual Attention IoU, used to measures attention concentration within object bounding boxes, defined as:
IoU = EigRan à
(10)
Σ)= dj
where Babj represents visual tokens within the target object's bounding box and â, denotes min-max normalized attention weights. Visual Attention Entropy measures attention concentration:
N
H
Σ
pi logp:
(11)
where pt represents the probability distribution from normalized attention weights.
In Figure 13, we present additional comparisons of attention distributions between SpatialLaddes and its base model Qwen2.5-VL-3B on the object size estimation task. The results demonstrate that, compared with the base model, Spatiall exhibits a more focused allocation of attention on task-relevant objects. This indicates that our training framework effectively guides the internal attention distribution of the model, thereby enhancing its inherent perceptual ability and supporting more reliable performance in spatial reasoning tasks.
D ADDITIONAL EXPERIMENTS
D.I CHAIN-OF-THOUGH TRAINING DYNAMICS
The analysis of cfain-of-thought reasoning dynamics, as illustrated in Figure 10, provides additional insights into the importance explicit reasoning processes spatial understanding tasks. While both the full model with chain-of-thought and its variant without reasoning components achieve comparable performance terms of accuracy reward curves during later training stages, significant differences emerge in training stability and convergence patterns. The oh enabled model demonstrates superior training stability, characterized by faster reduction in reward standard deviation and smoother convergence behavior. More critically, the actual performance trajectories
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La
45.0
Thinking
(Sedddds Actunacy (%)
Accuraty Reward
0.30
Spottall
0.30
375
wa. Thinking
Qwen2.55 VL.3B
0.15
35.0
32.5
Spurivll abdees 38
Thinking
0.05
30.0
500
1000
1500
2000
2500
0.00
300
1000
1500
2000
2500
500
1000
1500
2003
2500
Steps
Steps
Figure 10: Impact of thinking during training, Left: accuracy rewards; Middle: reward standard deviation; Right: VSI-Bench performance comparison.
Table 15: Performance comparison across spatial reasoning datasets. SI, MV, and VID denote single-image, multi-view, and video modalities, respectively.
Dataset
Modality
Size
VSI-Bench
SpaceR-15An 1516 (Ouyang al., 2025) Spatial-MI.LM MML..LLM-M0000O0000-aa0 et al., 2025)
VID
151,310
35.1
MV+VID
1120,000
40.0
Spatiall
SI+MV+VID
26,610
43.9
reveal distinct learning dynamics: the model without chain-of of-in-of-iie thothhttht reasoning reaches an early performance plateau and exhibits limited improvement thereafter, whereas the chain-of-thought variant maintains continuous performance enhancement throughout training, ultimately achieving superior final accuracy on evaluation benchmarks.
D.2 COMPARISON WITH OTHER SPATIAL DATASET
Table 15 demonstrates the effectiveness our dataset design through comparative analysis with existing spatial reasoning datasets. All models trained using supervised fine-tuning Qwen2.5- VL-3B as the base model to ensure fair comparison. Despite utilizing significantly fewer training samples (26,610 vs. 151,310 and R: 2=200,00), SpatialLadder-26k achieves superior performance on VSI-Bench, reaching 43.9% accuracy compared to 35.1% % for SpaceR- 151k and 40.0% for Spatial-MLLM-1200 This performance gain is attributed our comprehensive approach that integrates object localization tasks and spatial reasoning tasks across single-image, multi-view, and video modalities within unified framework, contrasting with previous datasets that focus on individual modalities limited combinations. The results validate that strategic dataset curation and progressive training can achieve better spatial reasoning capabilities with substantially reduced data requirements, highlighting the importance of data quality and training methodology over sheer dataset scale.
D.3 DATASET SCALING ANALYSIS
Figure 11 demonstrates the consistent scaling potential of our dataset across spatial reasoning benchmarks using Qwen2.5-VL-3B the base model for supervised fine-tuning. Overall performance increases steadily from 36.2% to 60.2%, while VSI-Bench improves from 29.4% to 43.9% as dataset scaling progresses from 0% to 100%. The sustained upward trajectories without saturation at full scale indicate substantial room for further improvement through continued dataset expansion. These scaling patterns validate the effectiveness highlight the potential for achieving even stronger spatial reasoning capabilities through strategic dataset augmentation.
dataset design larger scales and
D.4 IMPACT OF PROGRESSIVE TRAINING SEQUENCE
To validate our training paradigm, we conduct ablation studies using D-vn2.5--VL3BB as the base model with supervised fine-tur fine-tuning protocols. Figure 12 highlights the critical importance of training order in developing spatial reasoning capabilities. Our progressive perception-tor -o-ssttti training paradigm achieves 43.9% accuracy VSI-Bench, outperforming both spatial-only training (42.7%)
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50.0
Overall
70
VSI-Bench
Spacial-nnly
Perception Spatial
SPBench- MV
43.9
45.0
Performance (%)
42.7
422.5
Accuracy C
40.0
40
37.55
-35.0
30
0%
25%
5.%
75%
100%
30.0
Scaling (%)
Training Order
Figure 11: Dataset scaling analysis across spatial reasoning benchmarks.
Figure 12: VSI-Bench accuracy across different training order.
and mixed training approaches (40.7%). Crucially, the mixed training performance is even lower than the stage 2-only baseline, indicating that naively combining simple object localization tasks (Stage 1) with complex reasoning tasks (Stage 2) results in task interference.
The sequential approach that first establishing perceptual foundations through localization tasks, followed by spatial reasoning training, yields a 1.2% improvement over direct spatial training and a 3.2% gain over simultaneous mixed training. These results validate our hypothesis that systematic progression from basic perception complex reasoning creates more robust spatial understanding than alternative strategies. Our progressive structure effectively decouples conflicting objectives, establishing stable perceptual foundation first to facilitate the learning of complex spatial reasoning, and underscores that structured skill development through sequential training stages is essential for optimal spatial reasoning capabilities.
Table 16: VSI-Bench performance comparison across different KI. weight and reward scaling.
Here, 9KL. denotes the KI. weight, while Wiomaat and Waccuracy represent the scaling factors for the format and accuracy rewards, respectively.
0KL
Wormat
Waccuracy
VSI-Bench
0.01
1.0
1.0
45.7
0.01
0.8
0.2
44.9
0.01
1.0
1.0
45.2
0.04
1.0
1.0
45.2
D.5 RI. CONFIGURATION ANALYSIS
In our three-stage training framework, the reward coefficients and RL configurations are closely aligned with previously validated robust settings (Guo et al., 2025; Shen et al., 2025; Huang et al., 2025). This design isolates the impact of our training data and framework from the RL hyperparameters, allowing to demonstrate the intrinsic effectiveness of our method. To ensure completeness and reproducibility, we conducted additional experiments to analyze the sensitivity of the RI configuration, specifically the KI. weight and reward scaling (Table 16). While our default configuration yields optimal results, the model demonstrates strong robustness to variations in hyperparameters, maintaining consistently high performance across different settings.
Empirically, we observe that the format reward converges rapidly during the early training stages. Under this premise, the robustness to reward weights an inherent property of the GRPO mechanism: its group-based advantage normalization effectively cancels out the absolute scaling of the accuracy reward once the format reward stabilizes. This provides a theoretical explanation for why our RL stage is robust variations in reward scaling.
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Headphone
Laptop
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Chair
Figure 13: Additional examples of attention distribution comparison. For each example, the left panel shows the attention distribution of SpatialLadder, while the right panel shows that of Qwen2.5- VL-3B.
E LIMITATIONS AND FUTURE WORK
Our work presents several limitations. Due to computational resource constraints, our experiments are conducted exclusively on 3B-parameter models, leaving the scalability to larger models unexplored. Additionally, our Ladderll dataset has substantial room for scaling, with only 26,610 samples that may be insufficient for capturing the full complexity of spatial reasoning scenarios. The dataset's reliance primarily ScanNet scenes also introduces domain bias toward indoor environments, limiting generalization to diverse real-world scenarios. Furthermore, our three-stage progressive training framework follows fixed sequential structure that may not be optimal for all spatial reasoning tasks, lacking the flexibility to adapt to task-specific requirements.
These limitations suggest promising directions for future work. Scaling progressive training approach to larger models (7B, 13B, and beyond) could reveal additional performance gains and better understand the scalability of hierarchical spatial learning. Expanding the dataset both in
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scale and diversity- innorporatio larger sample sizes, outdoor landscapes, urban environments, and domain-specific imagery- would likely yield better performance and enhance robustness across varied scenarios. Additionally, developing adaptive training frameworks that dynamically adjust learning sequences based on task characteristic or model performance could improve efficiency. Finally, validating Spatiall_adder in real-world applications such robotics navigation and autonomous driving would provide valuable insights into practical deployment and identify areas for further improvement.
LLM USAGE
In this section, we clarify the role of large language models (LLMs) in preparing this work. We acknowledge that LLMs were employed exclusively for writing assistance and linguistic refinement in the preparation of this manuscript. These tools were utilized to enhance the clarity, grammatical accuracy, and academic style of the text while preserving all original research contributions, methodological approaches, and scientific insights developed by the authors. The language models served solely as writing aids to improve sentence structure, enhance readability of technical content, refine academic terminology, and ensure consistency in writing style throughout the manuscript.
It is important to emphasize that LLMs were not employed for research ideation, conceptual development, literature review, citation discovery, data analysis, experimental design, or generation of research hypotheses and conclusions. All research ideas, experimental work, data analysis, and scientific conclusions presented this paper originate entirely from the authors' independent intellectual work. The of LLMS limited linguistic enhancement and does not constitute contribution at the level of authorship. The authors take full responsibilityy for all content, including any text that was refined with LLM assistance, ensuring that the core intellectual contributions and scientific merit of this work remain wholly attributable to the listed authors.
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Reviews:
[{"review_id": "e0rlmgQnke", "reviewer": "Reviewer_fKYQ", "summary": "This paper introduces SpatialLadder, a progressive training framework for enhancing spatial reasoning in vision-language models (VLMs). The approach builds on a new dataset, SpatialLadder-26k, which includes 26k multimodal samples (object localization, single-image, multi-view, and video reasoning). The model is trained in three progressive stages: (1) supervised fine-tuning for perception (object localization), (2) spatial understanding through seven spatial dimensions (direction, distance, size, etc.), and (3) reinforcement learning with GRPO for complex reasoning. The resulting SpatialLadder-3B model achieves strong performance on multiple spatial reasoning benchmarks (VSI-Bench, SPBench-SI, SPBench-MV), outperforming GPT-4o and Gemini-2.0-Flash on average by 20.8% and 10.1%, respectively, with moderate out-of-domain generalization gains (7.2%).", "questions": "- How much of the performance gain comes from the new data versus the progressive training schedule?\n\n- Is Stage 3 RL critical, or can comparable performance be achieved by longer supervised training?\n\n- How sensitive is performance to the stage ordering or reward scaling?\n\n- Were out-of-domain benchmarks seen or related to the training domains (e.g., ScanNet overlap)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Extensive experimental validation, including ablations, training dynamics, attention visualization, and both in-domain and out-of-domain benchmarks.\n\n- The dataset is clearly defined and covers complementary modalities; the pipeline is systematic and reproducible.\n- Empirically strong; achieves state-of-the-art results on spatial reasoning tasks without architectural changes.", "weaknesses": "- The novelty is limited. The method primarily combines known strategies (curriculum learning, GRPO-based RL, multimodal fine-tuning) rather than introducing a new algorithmic insight or theoretical formulation.\n\n- The claimed “progressive construction of spatial intelligence” is not quantitatively justified beyond accuracy gains, no analysis on intermediate representation transfer or reasoning trajectory quality.\n\n- The reinforcement learning component is weakly analyzed: reward sensitivity, stability, and ablations on the GRPO configuration are missing.\n\n- Comparisons to alternative architectures (e.g., InternVL, LLaVA-Next) with equivalent compute budgets are absent, weakening generality claims.\n\n- The dataset scale (26k) is relatively small and domain-constrained (indoor ScanNet scenes). This limits claims of “general spatial intelligence.”", "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": "AQPTwSCPEA", "reviewer": "Reviewer_d8EG", "summary": "This paper addresses the persistent weakness of current Vision Language Models (VLMs) in spatial reasoning in terms of understanding object positions, distances, and relationships in 2D/3D scenes. The authors argue that this limitation stems from the lack of a hierarchical learning process bridging low-level perception and high-level reasoning. To tackle this, they propose a three-stage progressive training framework that mirrors human cognitive development:\n\n1. Stage 1 : supervised fine-tuning on object localisation tasks.\n\n2. Stage 2 : fine-tuning on a new multimodal dataset covering spatial reasoning tasks across images, multi-view setups, and videos.\n\n3. Stage 3 : reinforcement learning with chain-of-thought supervision (GRPO) to strengthen reasoning quality.\n\nThey also introduce SpatialLadder-26k, a curated dataset (26k samples) systematically covering spatial reasoning tasks across modalities. The resulting SpatialLadder-3B model (based on Qwen2.5-VL-3B) achieves state-of-the-art performance on spatial reasoning benchmarks,  including VSI-Bench, SPBench-SI and SPBench-MV, outperforming GPT-4o and Gemini-2.0-Flash by some margins.\nThe authors claim that this demonstrates the value of progressive spatial training for robust spatial intelligence in VLMs.", "questions": "1. Have the authors compared progressive stage-wise training against a single-stage joint fine-tuning using all Stage 1 + Stage 2 data to demonstrate that the “progressive” order is indeed beneficial?\n\n2. Given that Stage 3 yields only marginal gains, can the authors clarify what specific reasoning abilities are improved by the GRPO reinforcement phase, beyond formatting or answer consistency?\n\n3. Since the training and evaluation datasets share highly similar spatial QA formats and scene types, how do the authors ensure that the model’s gains reflect genuine spatial reasoning rather than memorization of task patterns?\n\n4. Qwen2.5-VL is a general-purpose multimodal model. Did the authors evaluate whether SpatialLadder retains its original non-spatial capabilities (e.g., captioning or OCR) after fine-tuning? This is assuming the fact that the authors did full fine tuning of the model and not LoRA, which mostly leads to catastrophic forgetting.\n\n5. Following from my previoous question, are all model parameters fine-tuned during the supervised and reinforcement phases, or are parameter-efficient methods (e.g., LoRA) used? How stable is training when applying full-model updates?\n\n6. Given that Stage 1 improves results only slightly, what evidence supports the claim that perceptual grounding through object localisation meaningfully benefits downstream reasoning?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed SpatialLadder-26k dataset is carefully curated and covers a wide range of spatial tasks from object localisation to multi-view and video reasoning, providing a systematic and high-quality resource for studying spatial understanding in VLMs.\n\n2. The three-stage progressive framework is conceptually clean and easy to reproduce. It makes sense, in theory, to have training stages the way the authors designed.\n\n3. The paper reports extensive experiments across multiple benchmarks, including new ones introduced by the authors, with consistent metrics, ablations, and qualitative analyses that make the empirical findings clear and verifiable.\n\n4. The resulting model achieves nice gains over comparable open-source and even proprietary models on several spatial reasoning benchmarks, demonstrating that targeted fine-tuning on well-structured spatial data can significantly improve performance.", "weaknesses": "1. Despite being framed as “progressive spatial reasoning,” the method largely reduces to a structured fine-tuning pipeline. Most of the gains appear to come from dataset design and task-aligned supervision rather than from a fundamentally new training principle or architectural innovation.\n\n2. The final stage adds negligible improvement, around one to two percent, and therefore does not convincingly demonstrate that the proposed reinforcement-based reasoning step meaningfully enhances spatial understanding beyond what supervised fine-tuning already achieves.\n\n3. While the authors ablate each stage separately, they never compare progressive versus joint training on all data. Without such a baseline, it remains unclear whether the sequential, staged structure is necessary or if a single fine-tuning step on the combined dataset would yield equivalent results.\n\n4. Both the dataset and the evaluation benchmarks share nearly identical task formats and question templates. This overlap makes it difficult to separate genuine reasoning improvements from specialization to the specific spatial QA style used during training.", "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": "4rjF1ErInW", "reviewer": "Reviewer_eN7r", "summary": "This paper tackles the challenge of spatial reasoning in Vision-Language Models (VLMs), positing that current models fail because they attempt to learn this skill monolithically rather than hierarchically. The authors introduce a \"perception-reasoning gap\" and propose a systematic, progressive approach to bridge it. Their contributions are twofold: 1) A new multimodal dataset, SpatialLadder-26k, containing over 26,000 samples for object localization, single-image, multi-view, and video spatial reasoning, built with a standardized pipeline. 2) A three-stage progressive training framework designed to first establish perceptual grounding (object localization via supervised fine-tuning), then develop spatial understanding across multiple dimensions and modalities, and finally strengthen complex reasoning using reinforcement learning (GRPO) with chain-of-thought. The resulting 3B parameter model, SpatialLadder, achieves state-of-the-art performance, significantly outperforming its base model as well as larger proprietary models like GPT-4o on a suite of in-domain and out-of-domain benchmarks. Extensive ablation studies validate that the progressive, curriculum-based approach is crucial to the model's success.", "questions": "1. The ablation study (Figure 5) indicates that Stage 3 (Reinforcement Learning) contributes less to the final performance (2.1% drop upon removal) compared to Stage 2 (Understanding SFT, 9.4% drop). Could you elaborate on the specific role of the RL stage? Does it primarily refine the structure and logical flow of the reasoning chains, or does it enable the model to solve problems that were previously intractable after Stage 2?\n2. The comparison in Appendix D.2 showing that the smaller SpatialLadder-26k dataset is more effective than larger datasets is a very strong result. Based on your analysis, which aspect of your dataset design do you believe is the most critical for this data efficiency: the inclusion of foundational localization tasks, the multi-modal coverage (SI+MV+Video), or the structured nature of the QA pairs?\n3. The proposed framework uses a fixed, sequential training order. Have you explored or considered more dynamic training curricula? For example, could there be benefits to cyclically reintroducing tasks from earlier stages or using a mixed-task approach once a foundational capability in each stage is achieved?\n4. The visual attention analysis is quite insightful. Have you investigated whether the more focused attention in SpatialLadder translates to better performance on multi-object relational tasks? For instance, when asked for the relative direction between two objects, does the model's attention map show precise focus on both entities simultaneously?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. **Clear and Well-Motivated Hypothesis:** The paper is built around the intuitive and compelling idea that spatial intelligence is hierarchical and must be learned progressively from perception to reasoning. This \"perception-reasoning gap\" hypothesis is clearly articulated and effectively motivated by a preliminary experiment showing that adding perceptual cues improves a base model's performance.\n2. **High-Quality Methodology:** The work introduces a comprehensive solution with two strong components. The SpatialLadder-26k dataset is systematically designed to cover a full spectrum of spatial tasks, addressing the fragmentation of existing resources. The three-stage training framework is a logical and direct implementation of the paper's core hypothesis, providing a clear recipe for building spatial reasoning capabilities.\n3. **Extensive and Rigorous Evaluation:** The experimental validation is exceptionally thorough. The authors evaluate their model on six different benchmarks, assessing both in-domain and, crucially, out-of-domain generalization. The comparison against a strong suite of open-source and proprietary models clearly demonstrates the effectiveness of their approach.\n4. **Convincing Ablation Studies:** The paper includes a comprehensive set of ablation studies that systematically dismantle the proposed framework to prove the value of each component. The analysis confirms the importance of each training stage, the multimodal data, the chain-of-thought mechanism, and, most importantly, the progressive training order itself. The dataset comparison in the appendix, which shows their smaller, curated dataset outperforms larger ones, is a particularly strong piece of evidence for their approach.", "weaknesses": "1. **Limited Model Scale Exploration:** The experiments are confined to a 3B parameter model. While the results are impressive for this model size, the paper does not explore whether these significant gains will translate to larger, state-of-the-art models (e.g., 7B+).\n2. **Dataset Domain Bias:** The dataset's reliance on the indoor scenes from ScanNet introduces a potential domain bias. The model's excellent performance might not fully generalize to outdoor environments or other visually distinct domains.\n3. **Incremental Novelty of Components:** While the overall system and the curriculum are novel and highly effective, the individual techniques used (supervised fine-tuning, GRPO for reinforcement learning) are established methods. The main contribution lies in the successful and systematic application of these components in a progressive manner to solve the spatial reasoning problem, rather than the invention of a new technique.", "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": "v1zP0wR6k6", "reviewer": "Reviewer_g2My", "summary": "This paper introduces SpatialLadder, a new 3B VLM in 3D spatial reasoning with a 3-stage training scripts, data collection pipeline and strong task performance. By incorporating this 3-stage training, SpatialLadder learns spatial knowledge and reasoning progressively and tops at spatial benchmarks while performing decently on OOD spatial tasks.", "questions": "I’m curious about how VLM performs on general VL benchmarks that are partially related to spatial reasoning like RealWorldQA, RoboSpatial-Home, MME-RealWorld-Lite. Will this 3-stage introduce significant catastrophic forgetting to VLM that hinders the general performance of the model?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "1. The 3-stage training pipeline looks reasonable and effective with carefully curated 26K data spanning across 4 domains. The data would be potentially beneficial to the community\n2. SpatialLadder-3B tops at in-domain and OOD spatial benchmarks \n3. Comprehensive ablations and visual analysis about attention maps and case study", "weaknesses": "1. Lack of ablation on the 3-stage training paradigm. As both stage 1 and 2 are SFT-ing over spatial data, though the type of questions and visual input maybe different, but what if combining the first two SFT stages into one as they both serve as broadening the spatial knowledge of base VLMs\n2. Only one model was trained. Will the model perform better if using another VLM or increasing the parameter size to 7B or larger?", "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)