Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model
Authors: Fuhao Li
Wenxuan Song
Han Zhao
Jingbo Wang
Pengxiang Ding
Donglin Wang
Long ZENG
Haoang Li
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-language-action Model
Representation Learning
Primary area: applications to robotics, autonomy, planning

Abstract: Vision-language-action (VLA) models have recently shown strong potential in enabling robots to follow language instructions and execute precise actions. However, most VLAs are built upon vision-language models pretrained solely on 2D data, which lack accurate spatial awareness and hinder their ability to operate in the 3D physical world. Existing solutions attempt to incorporate explicit 3D sensor inputs such as depth maps or point clouds, but these approaches face challenges due to sensor noise, hardware heterogeneity, and incomplete depth coverage in existing datasets. Alternative methods that estimate 3D cues from 2D images also suffer from the limited performance of depth estimators. We propose Spatial Forcing (SF), a simple yet effective alignment strategy that implicitly forces VLA models to develop spatial comprehension capabilities without relying on explicit 3D inputs or depth estimators. SF aligns intermediate visual embeddings of VLAs with geometric representations produced by pretrained 3D foundation models. By enforcing alignment at intermediate layers, SF guides VLAs to encode richer spatial representations that enhance action precision. Extensive experiments in simulation and real-world environments demonstrate that SF achieves state-of-the-art results, surpassing both 2D- and 3D-based VLAs. SF further accelerates training by up to 3.8× and improves data efficiency across diverse robotic tasks.

Published as a conference paper at ICLR 2026
SPATIAL FORCING: IMPLICIT SPATIAL REPRESEN- TATION ALIGNMENT FOR VISION-LANGUAGE-ACTION MODEL
Fuhao Lit.2,* Wenxuan Songh.<. Han Zhao Jingbo Wang
Pengxiang Ding
Donglin Wang Long 2. Haoang Li
'The Hong Kong University of Science and Technology (Guangzhou) Tsinghua University
WWestlake University *Zhejiang University 'South China University of Technology
ABSTRACT
Vision-language-acciin (VLA) models have recently shown strong potential in enabling robots to follow language instructions and execute precise actions. How- ever, most VLAs are built upon vision- langua- models pretrained solely on 2D data, which lack accurate spatial awareness and hinder their ability to operate in the 3D physical world. Existing solutions attempt to incorporate explicit 3D sensor inputs such as depth maps or point clouds, but these approaches face chal- lenges due to sensor noise, hardware heterogeneity, and incomplete depth cov- erage in existing datasets. Alternative methods that estimate 3D cues from 2D images also suffer from the limited performance of depth estimators. We pro- pose Spatial Foreing (SF), simple yet effective alignment strategy that implicitly forces VLA models to develop spatial comprehension capabilities without relying explicit 3D inputs or depth estimators. SF aligns intermediate visual embed- dings of VLAs with geometric representations produced by pretrained 3D foun- dation models. By enforcing alignment at intermediate layers, SF guides VLAs to encode richer spatial representations that enhance action precision. Exten- sive experiments in simulation and real-world environments demonstrate that SF achieves state-of-the-ar results, surpassing both 2D- and 3D-based VLAss. SF fur- ther accelerates training by up to 3.8x and improves data efficiency across diverse forciiorinngg github. io/
robotic tasks. Project page:
INTRODUCTION
The development of robotic manipulation hinges integrating semantic reasoning with accurate physical control in the 3D real world. Recently, vision-language-actte (VLA) models (Brohan al. 2022 Zitkovich et al., 2023 Bjorek et al. 2025 Black al. 2024). capable of instruction following and robotic action execution, have attracted significant attention. Most VLA models are built upon vision-language models (VLMs) (Liu et al., 2023b Chen al. 2023 Karamcheti al. 2024) to inherit semantic understanding capabilities and employ action tokenization (Kim et al., Song et al.) 2025b) and action experts (Wen et 2025
2024
al. 2024a) to output actions.
However, the VLM backbones of these 2D VLA models are pretrained solely on 2D visual modal- ities and lack precise spatial awareness (Wang et al., (2025c), making it infeasible adapt them to the 3D physical world. To address this, recent 3D VLAs have explored incorporating 3D modalities into inputs to exploit the rich geometric priors of the physical environment for precise manipulation. Several approaches (Chen et al., 2025 ei al. [2025a] Bhat et al. 2025a Li et al.) [225b;; Sun al.) 2025) use depth cameras or lidars to obtain depth maps or point clouds as input, which are then encoded and explicitly fed into the VLM backbone or diffusion head. However, some limitations make it difficult to develop a universal and scalable 3D strategy for this paradigm: (1) Reliable 3D
"Equal Contribution.
I Project Lead: songwenxuan wwweeexxxuu011eeeee gmaal. com
1 "Corrsponning Authors: haoangl ghkust-gz. -gz. edu and zeng longlong@eee z Lainghua edu.co

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Published as a conference paper at ICLR 2026
(a) Spatial Forcing (SF)
Action
(b) Performance
Training Efficiency
Test Accuracy
100
100
VLA Layer H1
94.2
Spatial Forcing
3.8X
78.3
Alignment
50
VLA Layer
2k
5k
201
30k
150k
W/b SF
W/
SpatiaVVLA
Foundation 3D Model
Image Tokenizer
Text Tokenizer
(c) Depth Probing
Pick black bowls the place It.
Image
Figure 1: Our proposed method, Spatial Forcing (SF), implicitly forces VLA models to acquire spatial-aware knowledge. (a) SF aligns intermediate visual embeddings of VLAs with geometric representations from pretrained 3D foundation models. (b) Our simple yet effective strategy yields significant improvements in training efficiency and test accuracy. (c) Depth probing proves that our SF brings spatial information into the aligned representations, further enhancing 3D perception.
sensor data is hard to acquire, as depth maps and point clouds obtained from sensors are often low- quality and inaccurate. (2) Robotic sensors vary in types, positions, and calibration status, which introduces substantial heterogeneity into the data. (3) Portions of existing large-scale robot datasets OOile al.] 2024b) Jang al., 2022) do not include depth information, which limits the potential of scaling. Other approaches (Qu et al. 2025) attempt to estimate 3D information from 2D images using depth estimators. Yet, their effectiveness is inherently limited by the performance of the depth estimator, which finally yields sub-optimal performance. Therefore, an essential question for this field now is how to implicitly develop VLAs' 3D perception and comprehension capabilities, thereby eliminating the dependence on explicit 3D sensor information or depth estimators?
To analyze this, we conduct a lightweight depth probing experiment: we extract the frozen visual embeddings from a mainstream VLA (Kim et al. 2025) and only train a DPT head (Ranftl et al., 2021 to predict the depth maps. As shown in Fig. Cc.. this observation proves that the original visual embeddings fail yield meaningful spatial structures, revealing a potential gap in the spatial reasoning capabilities of VLAS trained without external guidance. To bridge the gap, we propose Spatial Foreing (SF), simple yet effective alignment strategy that implicitly forces VLA models to acquire spatial-aware knowledge. Specifically, for auto-regressive VLAs with causal attention, action tokens generated conditioning the preceding visual and linguistic tokens. Intuitively, higher-quality visual tokens that contain richer spatial information can facilitate generating more precise manipulation actions. Recent advances (Huang al. 2025 Wang al.) 2024 Yu et al., 2024) have demonstrated the effectiveness of representation supervision. Inspired by this, as shown Fig. Ia), we align intermediate visual embeddings VLAs with external spatial representations extracted from pretrained 3D foundation models. Technically, to ensure multi-view consistency, we employ VGGT [Wang et al. 2025b) as the 3D foundation model to simultancously process robotic images and generate normalized spatial representations, which serve supervision signals. Overall, by aligning representations, we implicitly force VLAs to develop 3D comprehension capabilities.
Experiments conducted across multiple simulation environments and the real world validate the effectiveness of SF. Results (Fig. [(b)) on LIBERO and RoboTwin demonstrate that our SF achieves state-of-the-ar (SOTA) performance, surpassing previous strong baselines, including 2D and 3D VLAs. Additional experiments on training iterations and dataset sizes indicate that our method realizes 3.8x training while also exhibiting data efficiency with significantly less data. Finally, real-world experiments demonstrate the spatial comprehension and data utilization capabilities.
In summary, the contributions of this paper threefold:
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Published as a conference paper at ICLR 2026
We provide observation based depth probing, supported by an interpretable analysis, to highlight the insufficiency of spatial information in the visual embeddings of VLAs.
We introduce Spatial Forcing, a simple yet effective alignment strategy that implicitly enforces the integration of visual embeddings in VLAs with external spatial representations.
Experiments prove that method demonstrates enhanced performance, accelerated training speeds, and improved data efficiency across diverse robotic tasks.
2
METHOD
2.1 PRELIMINARIES
Vision-languaag--cttion Models. VLA models are built upon pretrained VLMs and generate ac- tions through specialized designs for action experts. A 0-parameterizeed VLM model employs mul- tiple causal attention layers in an auto-regressive manner to generate the next tokens, formed as 2, ~ Po <<)), where {-;}²= denotes sequence of tokens.
When adapted to VLA models, three modalities of information will be processed: vision, text, and action. The vision modality consists multi-view images captured by robots, which are transformed into N visual tokens {a=²" ]t= through pretrained visual encoders such as DINOv2 (Qquab et al. 2023) or SigLIP (Zhai et al. 2023). The text modality consists of task instructions, which are converted into M linguistic tokens {-f J Jt=1 by text tokenizer. Then, the VLA models generate K action tokens {>x²) 1t=1 )e=1 conditioned on the preceding visual and linguistic tokens:
2x po po (x) Ji=1: (ae I 1 #21)
Laction
Agt):
where L[.,1] denotes the training loss (e.g. LI, L2, or cross-entropy loss), G denotes the trainable action expert (e.g. two-layer MLP or flow-matching head (Lipman et al. 2022 Eq. ①I illustrates that the visual tokens as intermediate scene representations play a crucial role in generating action tokens and could be supervised properly.
Visual Geometry Grounded Transformer (VGGT), VGGT (Wang et al. 2025b) is feed- forward model that directly outputs various 3D attributes of a scene, including camera parameters, point maps, depth maps, and 3D point tracks, based series of 2D images. It is composed of transformer backbone and multiple prediction heads. To make the Transformer focus within each frame and globally in an alternate way, the model employs an Alternating-Attention mechanism that interleaves frame-wise self-attention and global self-attention. For each frame, local and global features integrated into unified latent representation, which is subsequently processed by of task-specific heads to produce corresponding 3D attributes. In our work, we argue that the la- tent representation extracted from the VGGT transformer backbone inherently encodes rich spatial information and is sufficient to serve as the 3D supervision signal.
2.2 MOTIVATION
Challenge. It is significantly challenging to bridge the gap between VLMs pretrained solely on 2D images and the underlying dynamic 3D structure of the physical world. Several approaches (Chen) al. [2025] Li al. 2025a Bhat et al.) 2025a Li al.) [025bb Sun et al.) 2025) (Fig. [a) incor- porate depth cameras to obtain depth maps as input, but the effectiveness is limited by low quality and limited quantity of 3D data. Other approaches (Qu et al.) 2025 Lin et al.) 2025) (Fig. 2())
attempt to estimate 3D information from 2D images, but their capabilities are limited by the perfor- mance of the depth estimators, which makes the policy sub-optimal. These challenges motivate our exploration of a universal and scalable training paradigm for 3D VLAs.
Observation. Since VLA models must capture 3D information and reason about the relative posi- tions between robots and objects to generate precise spatial movements, hypothesize that the 3D information is implicitly embedded within the visual embeddings VLAs. Such embeddings would allow action tokens acquire 3D cues through the auto-regressive mechanism during inference.
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Super- vision
Action
Action
Spatial Feature
Action
VLA Model
VidBot, PointVLA, GeoVLA, 3D-CAVLA
SpatialVLA, EVO-0
Spatial Forcing (Ours)
Visual
Input
Geometry
Grounded Transformer
(a) Explicit 3D Input depth camera or lidar (depth or point cloud)
Estimate 3D observation with E Explicit A ssistance
(c) Imlicitly Force model to develop spatial capabilities
Figure 2: Comparison among different paradigms for 3D VLAs.
Images
A lignment w/Aligmment
Images
Aignmment
w/ w/Alignment
GT
Prirsary
Wrist
LIBERO Simulation
Real-world Robot
Figure 3: Depth probing of the visual embeddings of VLAs. Embeddings learned solely from 2D images without alignment do not produce meaningful spatial structures. The aligned embeddings inherently contain rich spatial information, leading to better performance in depth probing.
To evaluate this hypothesis, follow linear probing (He et al.) 2020) and Wu al. (2025) to conduct lightweight depth probing experiment. Specifically, we freeze all the parameters of VLA model and only train a DPT head (Ranftl al. [021] to transform the visual embeddings of VLA to depth maps. This enables us to quantify the richness of spatial information embedded in the VLA representation space. As shown in Fig. 3 the probing results show that visual embeddings learned solely from 2D images do not produce meaningful spatial structures, suggesting limited capacity of the model to encode 3D structure information without explicit spatial input or guidance.
2.3 SPATIAL FORCING
Our main philosophy is to employ external supervision signals to supervise the visual tokens v To provide the signals with rich spatial information, first input a set of multi-view images I into the pretrained 3D foundation model VGGT f3D, which outputs the pixel-level spatial representations (T) for each image T. Additionally, these spatial representations embedding E to ensure that the supervised tokens preserve the critical position order within the auto- regressive process in the VLA. To align the VLA's per-pixel visual tokens with this 3D foundation model, we first process each I; with batch normalization I followed by two-layer MLP to ensure compatibility in feature dimension. Then we employ cosine similarity score to maximize the alignment between the visual tokens of VIA and the spatial representation signals:
added with extra positional
Latign
SIMLP I(x)), EJ,
where -].-] I denotes cosine similarity, (() is the spatial representations corresponding to the pixel location of visual token x.².
Additionally, as there multiple causal attention layers in the VLA model, there multiple x that can be gained after different layers. We found that supervising relatively deep but not the deepest layers is most effective in enhancing action performance. The reason is probably that the
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Table 1: Comparisons with state-of-the-art methods on LIBERO benchmark. Please note that methods in gray font incorporate extra depth or point cloud information from other sensors. Bold denotes the best performances among the methods without extra sensor inputs.
Spatial SR (%)
Object SR (%)
Goal SR (%)
Long SR (%)
Average SR (%)
Method
2D VLA
Diffusion Policy (Chi et al.
2023 /RSS'231
78.3
92.5
68.3
50.5
72.4
VLA (Zheng ct al.
2025
84.6
85.2
75.1
54.1
74.8
Octo Ghosh al. 2024
TRSS
24/
78.9
85.7
84.6
51.1
75.1
Openvla (Km et al. 2024
/C]
84.7
88.4
79.2
53.7
76,5
Dita (Hou et 2025
MCCV'25)
84.2
96.3
85.4
63.8
82.4
COT-VLA (Zhao et al.
2025
[CVPR'25]
87.5
91.6
87.6
69.0
83.9
To
FAST (Pertsch
2025 [RSS
96.4
96.8
88.6
60.2
85.5
To
Black et al. 2024] (55
96.8
98.8
95.8
85.2
94.2
UniVLA (Bu et al. 2025
96.5
96.8
95.6
92.0
95.2
Openvla-OFT Kim al.
2025
[RSS'25]
97.6
98.4
97.9
94.5
97.1
Explicit 3D VLA
SpatialVI.A Qu al.
2025
(RSS
88.2
89.9
78.6
55.5
78.1
GLo S Sun ct al.
2025
(25)
98.4
99.0
96.6
96.6
97.7
3D-CAVLA Bhat al.
2025b
[arXiv'25]
98.2
99.8
98.2
96.1
98.1
Implicit 3D VLA
Spatial Forcing (Ours)
99.4
99.6
98.8
96.0
98.5
deeper layers lose more vision-specific features, making them less amenable to the supervision of target spatial representations, which analogous the conclusion in Huang et (2024).
The final training objective combines both the standard training 3D foundation model alignment loss with weighting factor
loss for action generation and the
Csr Laction a Calign-
Overall, through SF, the VLA model acquires spatial reasoning capabilities implicitly and efficiently.
Model Inference. During inference, the VLAA model trained the manner operates identically to standard VLA without SF, introducing no additional structures computational overhead, thereby highlighting SF's high applicability.
SIMULATION EXPERIMENTS
3.1 EXPERIMENTAL SETUP
Simulation Environment. We evaluate our method on two widely-used simulation benchmarks, LIBERO (Liu et al. 2023a) and RoboTwin (Mu et al., 2025). LIBERO consists of four main task SEBERO--Oject LIBERO- Goal, and LIBERO-Long. Each task suite con- tains 500 expert demonstrations across 10 tasks investigate policy generalization to different spa- tial layouts, objects, goals, and long-horizon tasks. ToboTwin is real-to-sim bimanual benchmark. It contains easy setting with in-domain layout and hard setting with domain randomization, in- cluding scene clutter, diverse background textures, lighting variation, and varied tabletop heights. We evaluate methods diverse tasks. We report the success rates (SR) evaluation metrics for these two benchmarks.
suites: LIBERO-SS LIBEOO-Spatil,,
Base Models and Implementation Details. We follow Kim al. 2025 and Black al. (2024) to employ VLe VA-OFF on LIBERO, and To on RoboTwin, as base models. VLA LLA-OP -OFT uses the Prismatic VLM (Karamcheti et al. 2024) pretrained the Finnode-Emtodieemmeen dataset
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(0 O'Neill et al. 2024b) the VLM backbone and the fused vision backbone (both SigLIP (Zhai et al. 2023) and DINOv2 (Oquab et al., 2023)) as the vision backbone. We train our SF based on 8 NVIDIA HI0O for 150k iterations to compare with other methods. wo the 2024) as VLM backhone. We train our SF based on To with LoRA (Hu al., 2022) NVIDIA HTOO for 30k iterations. To ensure fairness, all training and evaluations
Open VLA-OFT
PaliGemma (Beyer et al.
follow the official settings.
3.2
COMPARISONS WITH STATE-OF-THE-AR METHODS
DP
80
(F(rss))
60-
Success
20
0-
Fasy
Easy
Hard
Easy
Hard
Easy
Hard
Easy
Hard
Easy
Hard Lift Pot
Easy Place Shoes
Easy Average
Move Playingcord
Turn Switch
Click Bell
Open Micrewave
Beat Black
Figure 4: Comparisons with state-of-the-art methods on RoboTwin 2.0 benchmark.
LIBERO. Each task is evaluated for 500 trials under random seeds. Tab. shows that SF gets the best performance across all four tasks. Specifically, strictly following the same setup of Open OFT to use both primary and wrist camera images, our method outperforms them quite lot. Be- sides, for long-horizon tasks on LIBERO-Long, our method also demonstrates strong capabilities to complete long- long-horizon tasks. Please note that, without requiring additional 3D inputs, our SF achieves comparable performance with those methods that benefit from extra 3D sensor inputs (e.g., Geo VLA and 3D-CAVLA).
Each easy task is evaluated for 100 trials, and each hard task is evaluated for 300 trials under random seeds. Fig. shows that our SF achieves the highest average success rate and yields substantial improvements over the base model To across all tasks, which demonstrates its effectiveness in enhancing the spatial awareness. Moreover, the obvious improvement on hard tasks indicates that SF enables the model to accurately capture object locations and focus on their relative spatial relationships, rather than relying on shorteut correlations such as background lighting cues.
Twin.
3.3
COMPONENT- WISE ANALYSIS
We further investigate the effect of SF by varying multiple model components and training condi- tions, Below, we provide a detailed analysis each ablation. We take OpenVLA-OFT (Kim et al. 2025) the base model and conduct experiments the LIBERO benchmark with single H100 because of limitations of computational resources.
Target Representation. We examine whether the effectiveness stems primarily from our proposed paradigm or from the quality of the target representations, shown in Tab. 2 Both SigLIP (Zhai 2023) and DINOv2 (Oquab al. 2023) are widely adopted vision backbones pretrained on large-scale 2D image datasets. SigLIP excels at semantic understanding through robust image- text alignment, whereas DINOv2 offers stronger visual grounding owing its fine-grained spatial representations. VGGT (Wang et al. 20256), trained on 2D-3D paired datasets, possesses powerful spatial perception abilities for predieting 3D attributes. All models with SF alignment get higher success rates compared to the base model, which shows that visual embedding alignment serves as general paradigm to implicitly enhance visual perception. Using VGGT as the target representation yields the highest success rates, demonstrating that compensating for the lack of 3D understanding crucial for VLA models. Furthermore, we find that adding positional embedding (Ranftl et al. 2021) to the target representation significantly enhances performance on long horizon tasks. The reason is that the aligned visual embeddings are utilized in an regresegress manner within VLA, where the relative position of tokens plays critical role.

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Table 2: Component Analysis on LIBERO benchmark. PE denotes positional embedding. Bold means the best performance. Experiments are conducted on IxH100.
Target Representation
Aligned Layerth
Training Iterations
Training Data
Spatial SR (%)
Object SR (%)
Goal SR (%)
Long SR (%)
Average SR (%)
x
x
150K
100%
96.8
94.8
92.8
86.2
92.7
SigLIP
24
150K
100%
95.2
94.8
94.0
91.8
94.0
DINOv2
24
150K
100%
93.4
95.2
93.8
93.8
94.1
VGGT w/o PE
24
150K
100%
97.8
100.0
96.6
84.4
94.7
VGGT
24
150K
100%
97.2
99.2
96.8
94.2
96.9
VGGT
150K
100%
96.8
99.4
99.0
83.0
94.6
VGGT
150K
100%
96.2
98.4
95.6
92.4
95.7
VGGT
16
150K
100%
97.4
98.8
95.8
83.2
93.8
VGGT
24
150K
100%
97.2
99.2
96.8
94.2
96.9
VGGT
32
150K
100%
98.8
99.4
96.2
84.8
94.8
VGGT
24
2K
100%
70.6
89.8
87.0
43.4
72.7
VGGT
24
5K
100%
93.8
94.8
94.6
66.6
87.5
VGGT
24
20K
100%
96.8
99.0
93.8
85.2
93.7
VGGT
24
50K
100%
97.0
99.0
96.2
93.6
96.5
VGGT
24
150K
100%
97.2
99.2
94.2
96.9
VGGT
24
150K
1%
32.8
67.8
44.8
23.6
42.3
VGGT
150K
73.2
83.4
80.6
66.0
75.8
VGGT
24
150K
100%
97.2
99.2
96.8
94.2
96.9
Alignment at Different VLA Layers. We further investigate the effect of supervising different transformer layers of VLA, shown Tab. 2 Our VLM backbone (Karamcheti et al., 2024) contains 32 causal attention layers. The results indicate that supervising relatively deep but not the deepest layers yields the most effective alignment. This is because supervising deep features implicitly enforces shallow features be aligned more closely with spatial representations, thereby yielding improved spatial understanding at the global level. In contrast, constraining shallow features may cause the already aligned representations gradually lose spatial information in subsequent layers. Besides, the visual and language modalities of VLA tend to converge into a modality-agnosti space with the layer increasing (Huang al. 2024). Therefore, the last several layers lose more vision- specific features, making them less amenable to the supervision of target vision representation. This trade-off allows the 24th layer to achieve the best alignment performance.
Training efficiency. We analyze whether SF helps improve training efficiency, shown in Tab. and Fig.
().. We report the task success rates vs. training iterations before and after representation
100
Success ess Rate (%6)
3.8x
90
5.9x
Success
60
Simllrr shaps
w/o Alignment Alignment
w/o Alignment Alignment
70
65
2k
Sk:
20k
50k
150k
1%
5%
33%
100% Primery Target
CIWrst Target
Training iterations
The training data
Primary Alignment
Alignment
Primery wi Aliggment
Wrist wi Alignment
(a) Training efficiency
(b) Data efficiency
(0) I-SNE visualization
Figure (a) We report the success rates vs. training iterations before and after representation alignment. (b) We report the success rate vs. training data before and after representation alignment. (c) The aligned representation exhibits almost the same distribution shape as the target.

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alignment. The results illustrate that training with alignment significantly speeds up the convergence, achieving the same success rates 3.8x more quickly than the base model. We hypothesize that our SF serves efficient learning pathway, enabling VLA models to acquire visual understanding and rapidly capture essential spatial relationships.
Data efficiency. We analyze how SF helps improve data efficiency, shown in Tab. and Fig. ()). We uniformly sample %,, 5%, and 33% of the entire dataset to create mini datasets. To ensure fairness, we use the cosine-annealing rather than multi-step training scheduler. SF reaches 75.8% success rates with only 5% data. also achieves 25.8% higher success rates in terms of the same data amounts and reaches 5.9x more efficient terms of the same success rates. Since the target representation is derived from pre-trained perception models, it can capture essential scene-genera information. With the guidance this target representation, the VI.A can learn the core features with little conductive bias from only small amount of robotic data, thereby improving task success rates. Given the scareity of real-world data, this capability particularly valuable for robotic applications.
The t-SNE visualization. We further visualize the degree of representation alignment. As shown in Fig. C(c), can be seen that after alignment, the VLA feature exhibits almost the same distribution shape as that of the target, while its cluster center remains independent from the target. This demon- strates that SF not only forces the VLA model to learn spatial comprehension ability but also ensures that its visual modality preserves original representational identity. More in-depth explanations can be seen in Appendix Sec. D
4 REAL-WORLD EXPERIMENTS
Action Sequenace
Rujurt Armis
Putte
(c) Setup
Task Variaton
5R (%)
15.0
10.0
67.5
30.0
62.5
47.5
35.0
42.5
Stack Glass Cups (light variation)
Grasp Right-side Vegetable (target object variation)
Place Green Block (height variation)
Lift Pot
(b) Dual-arm tasks
(a) Single-arm tasks with variation conditions (w/o SF vs w/ SF)
(w/o SF uss w/ S/)
Figure 6: Real-world Experiments. (a) A set of single-arm tasks across various visual and spatial conditions. For each task, train unified model to face all variations and report the success rate. (b) Dual-arm tasks to measure the spatial horizontal balance ability. (c) Top- view robot setup.
We conduct real-world experiments to evaluate the effectiveness and data efficiency of our SF across highly variable environments.
4.1 EXPERIMENTAL SETUP
As shown in Fig. CC., we conduct real-robot experiments on the bimanual AgileX platform. Each arm consists of 6-DoF Piper manipulator and 1-DoF gripper. A primary camera and wrist
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cameras are installed on the body and the arms. We design comprehensive set of tasks that covers various axes of spatial capabilities: stack glass cups with light variation, grasp right-side vegetable with target object variation, place green block with height variation, and lift pot with new em- bodiments. To examine the data efficiency, our model is trained on only 40 demonstrations for single-arm tasks and 20 for bimanual tasks. For evaluation, we test 10 trials per variation (40 trials in total) for each single-arm task, and 20 trials for the dual-arm task. We report the success rate (SR) of each task as the evaluation metric.
4.2 RESULT ANALYSIS
Fig. shows that our SF achieves higher success rates across all tasks, showing considerable im- provements in data efficiency. This ability is critical for real-world deployment. Specifically, in the stack glass cups task, transparent cups reflect varying surrounding lighting colors, making highly deceptive. Our SF reaches 47.5% higher success rates than the base model because SF captures the underlying spatial relationships rather than overfitting the spurious correlations. In the grasp right-side vegetable task, different target objects require distinct gripper poses and clamping widths. Our performance demonstrates an understanding of the object's 3D appearance. For the place green block task, varying placement heights require precise estimation of spatial height information. Ben- efit from spatial feature learning. SF achieves an 85% success rate. Results of the bimanual lift por task indicate the adaptability in the new configuration well spatial awareness of the pot's hori- zontal balance to prevent tilting. The performances across all tasks in Fig. 6 a) and (b) demonstrate that SF substantially improves task success rates, indicating its strong spatial comprehension and data utilization capabilities.
5 RELATED WORK
Vision Aisionllagguage-attion Models. Given natural language instructions and scene observations, VLA aims to produce executable actions for robots. Prior works leverage vision and language foundation
models to enhance robotic capabilities for low-level object localization (Stone et al. 2023 Gadre
ct al. 2023) as well as high-level reasoning and planning (Brohan et al. 2022 Zhao et al.
2023
Huang et al., 2023). With the development of VLMs (Liu al. 2024a] Karamcheti et al.
2024
Beyer et al.] 2024). numerous VLA research (Zitkovich et al.
[023] Deng et al.) 2025 Li et al.
2024a] Song et al. 2025a; Li et al. (024b) utilize VLMs with action experts to learn the general- izable action and language knowledge. VLE (Kim et al. 2024) proposes the first open-source VLA model pretrained on large-scale robotic datasets (O Neill et al. 2024b). However, these meth- ods primarily focus on 2D image information and lack an accurate comprehension of the 3D physical world. Recent studies enhance the spatial perception of VLAS through depth estimation (Zhen et al,, 2024 Qu et al. 2025 Zhang et al., 2025), ),. point cloud injection (Li et al. 2025a| Sun et al.) 2025). and spatial projection (Li et al. 2025b) Arrus et al., 2025). Nevertheless, these approaches exclu- sively enrich the visual inputs of VLAS with spatial information, while overlooking that the visual embeddings as intermediate scene representations also play a crucial role in generating action to- kens. In contrast, we argue that aligning the visual embeddings of VLAS with external powerful 3D) representations (Wang et al.) 2025b) is an efficient supervision to guide VLAs with spatial learning.
Representation Supervision. The latent representations of models can be supervised through re- construction or alignment to facilitate downstream task adaptation. As for reconstruction-ban -based supervision, L-DAE (Chen al. (024)) and Genhancer (Ma al., (025) enable generative models to enhance visual representations. ROSS (Wang et al.) 2024 2025a) supervises the visual embed- dings of VLMs for VQA tasks by employing a denoising architecture (Ho et al. 2020)) to reconstruct input images. Recon VLA (Song et al. (025c) adopts reconstructions on the gazing area as a super- vised objective to guide VL.As in allocating attention to target objects. However, the reconstruction supervision may not be suitable for VLAs to learn effective representations, as it fails to filter out re- dundant details (LeCun) 2022 Assran et al. 2023). As for alignment- based supervision, (Wu et al.) 2025) and REPA (Yu et al. (024) directly align the intermediate hidden states of image and video generation models with external pretrained visual encoders (Oquab et al. 2023 Wang et al. 20256). 3DRS (Huang al. 2025) introduces 3D representation supervision strengthen its spatial ground- ing capability. The cosine similarity is commonly used as a loss function to aligns hidden states at the pixel-patch level independently. Our work also shares some similarities, where we follow the

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Published as a conference paper at ICLR 2026
alignment- based paradigm to guide the intermediate representations of models towards geometry- aware structures. Specifically, owing to the global attention across patches within the external 3D foundation model (e.g. VGGT), this alignment yields stable 3D representations for the whole scene.
CONCLUSION
In this paper, we investigated how to implicitly force VLAs to develop 3D perception and com- prehension capabilities. We begin with a lightweight depth probing experiment to investigate the insufficiency of spatial reasoning in current VLA models. Consequently, ing (SF), a simple yet effective method that aligns the visual embeddings in VLAs with external spatial representations extracted from 3D foundation models. Finally, the simulation experiments prove that SF can enhance performance, accelerate training speeds, and improve data efficiency. The real-world experiments prove its spatial comprehension capabilities across diverse robotic tasks.
propose Spatial Forc-
ACKNOWLEDGMENTS
This work was supported in part by the Natural Science Foundation of China under Grant 62403401, part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A151501 1992, in part by the Guangdong Provincial Project under Grant 1024QN11X121 in part by the Shenzhen Science and Technology Program under Grant CIGIZID202407991410000 and in part by the AI Research and Learning Base of Urban Culture under Grant 2023WZJD008.
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A THE USE OF LARGE LANGUAGE MODELS (LLMs)
In preparing this paper, large language models (LLMs) were utilized solely as general-purpose writing aids for light proofreading and language polishing. This assistance was limited to minor improvements in grammar, phrasing, and style. All core intellectual contributions, including the research ideas, problem formulation. methodology, experimental design, data analysis, code devel- opment, and substantive writing, were conceived and executed by the authors. No LLM was used to generate original content, design experiments, analyze data, write code, or draft any part of the manuscript. The authors thoroughly reviewed and verified all text, and they take full responsibility for the entire content the paper. The LLM should not be considered an author or contributor.
B REPRODUCIBILITY STATEMENT
We have taken several measures to ensure the reproducibility of our work. The main paper provides detailed descriptions of the model architecture, training objectives, evaluation protocols, and imple- mentation details. Additional dyper-parameee presented in the appendix. Furthermore, we include demo source code package the supplemen- tary materials to facilitate the reproduction of our results.
settings and real-world data collection settings
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Table 3: weight factor of alignment loss.
0
0.02
0.1
0.5
2.5
12.5
SR (%)
73.2
92.2
92.8
93.6
86.6
81.2
C
WEIGHT FACTOR
is used control the relative weight of the alignment loss in Eq. (4) Appro- priate weight can implicitly force the model to develop spatial comprehension capability. However, the excessively large weight may destabilize the VLA visual modality and interfere with the original robot action prediction. As shown in Tab. 3 the model performs best under the a = 0.5, which is the default setting all other experiments.
The
D EXPLANATIONS OF T-SNE RESULTS
This section provides demonstrate the effectiveness of our SF. T-Distributed Stochastic Neighbor Embedding (t-SNE) is non-linear dimensionality reduction technique primarily used for visualizing high-dimensional fea- tures into a low-dimensional space (typically 2D or 3D). It works by modeling the similarity between high-dimensional data points as conditional probability. Specifically, computes the probability that a point æ; would pick another point 2j neighbor, based on the Gaussian distribution cen- tered on Ti.
detailed explanation of the t-SNE visualization used in Sec. 3.3
The distribution shape of a t-SNE cluster reveals the internal relative similarities and discrepancies among the features within that set. similar distribution shape indicates that the relational structure between features has become isomorphic. In our results, the VLA feature with SF alignment exhibits almost the same distribution shape as that of the target. This means that, by forcing the VLA model's features adopt similar relational geometry, mapping function. is forcing the VLA model to learn the underlying manifold of the target's spatial representation. The VLA model learns not just where individual features should be, but how the entire feature space is structured with respect to spatial concepts.
SF method is doing more than just simple linear
The center of t-SNE cluster is roughly the densest, dependence between two cluster centers indicates that their local structures are preserved separately. In our results, the center aligned VLA features remains independent from that of the target, which signifies that the alignment process has not caused a representational collapse. If the alignment were merely forcing the VLA features to exactly replicate the target features, the two clusters would over- lap entirely. The distinct cluster centers demonstrate that while the VLA features have adopted the relational structure of the target, they have done so without discarding information that is unique to their own modality.
typical region of that feature set. The in-
E REAL-WORLD DATA COLLECTION
During data collection, we use the master arms in a teleoperated manner to guide the puppet arms to finish tasks. Camera images and absolute joint angles are recorded as task-specific datasets at 30Hz. During training and inference, the model is fine-tuned separately for each task to control the puppet arms in task execution.
F DETAILS OF COMPARED MODELS
To (Black et al. 2024) To is a vision-language-action model for general robot control that inte- grates pre-trained vision vasion-language model (VLM) with novel flow matching action expert. This architecture enables the model to produce continuous, high-frequency actions. The model trained with two-stage recipe: broad pre-trate tre-training a large-scale, diverse, cross-embodinent dataset, fol- lowed by optional fine-tuning on high-quality data. In evaluations, An excels at following language commands and significantly outperforms methods designed specifically for dexterous manipulation
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tasks. Furthermore, the model can be adapted to master exceptionally complex, multi-stage tasks that take 5 to 20 minutes to complete, such as folding laundry and bussing cluttered tables.
Openvla-OFT (Kim et al., 2025) Openvla-OFT introduces an Optimized Fine-Tume Tuni-Tning (OFT) recipe designed to enhance both performance and inference efficiency of VLAs when adapting them to specific robotic tasks. The recipe integrates parallel decoding, action chunking. a continuous ac- tion representation, and simple L1 regression objective to improve inference efficiency. For tasks requiring precise language understanding, the recipe is further augmented with FiLM (Feature- wiie Linear Modulation) (Perez et al., 2018) to strengthen language grounding. On the LIBERO simula- tion benchmark (Liu et al., (0022200)) Open
boosts the success rate to 97.1 % while increas- ing action generation throughput by 26x. In real-world evaluations
bimanual ALOHA robot (Zhao et al. 2024), it outperforms strong fine-tuned VLAs like To (Black et al. (024)) and RDT-IB (Liu et al., 2024b), as well as policies trained from scratch, by up 15% (absolute) in average success rate on dexterous tasks.
Diffusion Policy (Chi et al. 2023) Diffusion Policy is a visuomotor policy that represents the robot's behavior conditional denoising process. Instead of directly predicting action, Diffu- sion Policy learns the gradient of the action distribution and iteratively refines a randomly sampled action through series of denoising steps with respect to the gradient field. This formulation enables the model to gracefully handle multimodal action distributions and high-dimensional action spaces, leading to impressive training stability. Across 15 different tasks from 4 different robot manipula- tion benchmarks (Florence et al., 2219;; Gupta et al. 2019: Mandlekar et al. 2021 Shafiullah et al.) 2022]. Diffusion Policy consistently outperforms existing state-of-the-arr robot learning methods
with an average improvement of 46.9%.
VLA (Zheng et al. 2025) TLa provides a visual trace prompting technique that en- hances the the-shelf point tracker Co- Tracker (Karaev et al.] (024) to generate trajectories of the robot's past movements, which are then visually overlaid onto the current observation as an additional input prompt for the VLA Model. Evaluations show that TraceVLA outperforms the OpenVL. (Kim) al., 2024) baseline by 10% in the SimplerEnv simulation and by 3.5x on real- world robot tasks, showcasing robust generalization across diverse embodiments and scenarios.
awareness of generalist robotic policies. The method employs an off-
Octo (Ghosh et al. 2024) Octo is an open-source, transformer-based model for robotic manipu- lation pretrained on 800k trajectories from the Open X-Embodiment (OXE) dataset OO'Neill al., 2024a). The architecture pairs a large transformer backbone for processing multimodal inputs with lightweight diffusion head that generates expressive, continuous actions. The model can be in- structed via language commands or goal images and demonstrates strong zero-shot performance, outperforming RT-1-X (O'Neill et al. (2024a) by 29% on average. Its compositional design makes it versatile initialization for data-cfficient finetuning. On average, finetuned Octo policies outper- form training from scratch by 52%, successfully adapting to novel sensors, new action spaces, and entirely new embodiments. Crucially, this adaptation is achieved with only 100 demonstrations and a few hours of training on a single consumer GPU.
Openvla (Kim et al. 2024)
VLe is a 7B 7B-parameter, open-source Vision-Laanuuag-Acttion (VLA) model designed for generalist robotic manipulation. The model's s architecture is built on Llama backbone Touvron et al., (023) combined with a powerful visual encoder that fuses fea- tures from both DINOv2 (Oquab al. (003) and SigLIP (Zhai et al. 2023), enabling strong spatial reasoning and semantic understanding. Pretrained on a diverse collection of 970k real-world robot demonstrations from the Open X- X-Embodiment dataset (O'l et al.] 2024a). Open VLA outper- forms the closed-source 55B RT-2-X model (O'Neill et al. 2024a) by 16.5% in absolute success rate across multiple robots and 29 tasks the WidowX (BridgeData V2) (Walke al., 2023) and Google Robot platforms. VLe also introduces robust and efficient fine-tuning strategies, such as LoRA (Hu et al. 2022). which allow the model to be quickly adapted new tasks and robots on consumer-grade grade GPUs
Dita Hou et al. 2025) Dita is a scalable framework for generalist robotic learning that leverages a Diffusion Transformer (DiT) (Peebles & Xie 2023) to directly denoise continuous action sequences.
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The architecture features "in-context conditioning" mechanism where causal transformer di- rectly processes raw visual tokens from historical observations to inform the denoising of future ac- tions, enabling fine-graines alignment and explicit modeling of environmental nuances. Pretrained on the large-scale OXE dataset, the lightweight 334M Dita model achieves state-of-the-art or com- petitive performance across extensive simulation benchmarks like LIBERO, CALVIN (Mees al.) 2022), and ManiSkil12 (Gu et al., 2023). Furthermore, it demonstrates robust real-world adaptation, successfully executing complex, long-horizon manipulation tasks with just 10-shot finetuning.
CoT-VLA (Zhao al. 2025 CoT-VLA is a VLA model that incorporates visual chain-of- thought (CoT) reasoning. where it first auto-regressively generates future subgoal image as an intermediate reasoning step before predicting the action sequence. The model built upon the VILA-U multimodal foundation model (Wu et al. 2024) and features hybrid attention mechanism, using causal attention for subgoal image generation and full attention for multi-step action predic- tion. The training dataset includes not only robot demonstrations from the Open X-Embodiment dataset but also action-less video datasets like PPIC-KIT (Kapidis et al., 2019), allowing the model improve visual reasoning from unlabeled sources. In evaluations, VLA achieves a 6% average improvement over Open VLA on the LIBERO simulation benchmark and a 17% im- provement in real-world manipulation tasks.
Fo-FAST (Pertsch et al. 2025) FAST introduces efficient action tokenization method for VLA models that enables the training of auto-regressive policies on high-frequency, dexterous manipula- tion tasks where previous binning schemes failed. The approach, named Frequency-space Action Sequence Tokenization (FAST), leverages the discrete cosine transform (DCT) to convert action tra- jectories into the frequency domain, where the signal's core information is naturally concentrated into a few low-frequency coefficients. This method effectively compresses actions into a compact set of discrete tokens, significantly reducing redundancy. When integrated with the To backbone, the resulting auto-regressive policy (Tg- (To-FAST) matches the performance of the original To model on complex, long- horizon tasks like laundry folding, while reducing training time by up 5x.
UniVLA (Bu et al., 2025) UniVLA introduces a framework for generalist robotic policies that learns unified, task- cusk-centric latent action space from videas, uniquely enabling it to leverage diverse data sources (including human videos) without explicit action labels. The core of the method is an unsupervised latent action model that uses VQ-VAE to discretize task-relevant dynamics from paired video frames within the DINOv2 feature space. These quantized latent actions then serve as pscudo-labels to pretrain an auto-regressive vision-language policy. In evaluations, UniVLA significantly outperforms VLe achieving a 95.2% success rate on the LIBERO benchmark (an 18.7% absolute improvement) and 36.7% absolute improvement in real-world deployment tasks, while using less than 1/20 of the pretraining compute.
SpatialVLA (uu et al., 2025) SpatialVLA is spatial-enhanced VLA model designed to im- prove 3D spatial understanding. The architecture introduces two key innovations: Ego3D Position Encoding, which injects 3D spatial context from depth information into the input observation of PaliGemma 2 backbone (Steiner al.) 2024), and Adaptive Action Grids, a novel action repre- sentation that discretizes continuous robot movements into adaptive spatial grids based on the data distribution. Pretrained on 1.1 million real-worke robot episodes, Spatial demonstrates superior zero-shot performance and efficient adaptation capabilities. In extensive evaluations across 24 real- world tasks and 3 simulation environments (Li et al. 2024c] Liu al. 2023a Walke et al. 2023) ) achieves oate--of-theaar results, significantly outperforming models like Open VLA and Robo VLM (Li et al.] (2024b), particularly in tasks requiring precise spatial reasoning and generalization to new robot setups.
GeoVLA (Sun et al., 2025) GeoVLA is VLA framework designed to improve robotic manip- ulation by explicitly integrating 3D geometric information alongside standard 2D visual inputs. Its novel dual-path architecture features a standard VLM for processing 2D vision and language, to- gether with a custom Point Embedding Network (PEN) that extracts geometric features from point clouds derived from depth maps. These multimodal embeddings are then fused by 3D-enhanced Action Expert (3DAE) to generate precise and continuous actions. In evaluations, Geo VLA achieves state-of-the-ar performance, outperforming VLAFFLA the LIBERO benchmark and Dita
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on ManiSkill2 (Gu et al. 2023). In real-world experiments, it demonstrates superior robustness to 3D variations such as changes in object height, scale, and camera viewpoint, outperforming strong baselines like To by 28.8% in average success rate.
3D-CAVLA (Bhat et al., 2025b) 3D-CAVLA introduces a framework that enhances Vision- Language-Aetin Action models with improved 3D spatial awareness and reasoning boost generalization on unseen tasks. Built upon VLenVLA-OOE the model integrates three key modifications: chain- of-thought-style narrative prompts to enrich task context, 3D features derived from point clouds to improve depth perception, and task-oriented region-of-interest pooling to focus visual attention. The model achieves near-perfect 98.1% average success rate standard LIBERO tasks. Furthermore, it demonstrates significant 8, 8.8% absolute improvement over VLA-OLA-OFT on newly proposed benchmark of 10 zero-shot tasks derived from the LIBERO environment, highlighting its superior generalization capabilities.
G ADAPTIVELY CHOOSING ALIGNMENT DEPTH LAYER
We develop an adaptive strategy automatically choose the alignment depth layer of VLAs, and we find this strategy is effective. Specifically, following the core idea of MOE soft gating mechanism (Li et al. 2019; Shazeer et al. 2017). firstly we employ a trainable weight matrix M, E RLXD, where L is the total amount of VLA layeey (e.g., 32 for LLaVA), D is the feature dimension. Then, pool the VLAs' visual tokens along the token axis:
P
2 4
(5)
where {x²)) is N visual token of VLAs. If the visual tokens 'x.' are gained at the le layer of VLAs, we denote the corresponding pooling feature P(I), where = {1, L}.
Next, calculate the gating score scalar for each layer:
8i ((I).
Finally, we use the mixtual visual feature to calculate the align loss:
exp(si) v |am].
F Falign
j== exp(s;)
We report the updated success rate on LIBERO. As shown in the Tab. below, the adaptive layer se- lection strategy shows clear improvements. And we provide the gating score distribution histograms across layers in Fig.
Table 4: The Ablation of Supervising Different Transformer Layers of VLA.
Aligned Layerth
Spatial
Object
Goal
Long
Average
96.8
99.4
99.0
83.0
94.6
96.2
98.4
95.6
92.4
95.7
16
97.4
98.8
95.8
83.2
93.8
24
97.2
99.2
96.8
94.2
96.9
32
98.8
99.4
96.2
84.8
94.8
adaptive
98.6
99.4
98.8
95.4
98.1
H ADDATIONAL REAL-WORLD SETTINGS
H.1 COMPLEX COLOR, CLUTTER, INSTRUCTION CHALLENGES
As shown in Fig. the target objects include red bell pepper, green bell pepper, red carrot, or green cube. This experimental setting is provided in the revised appendix. Models trained on 40 demonstrations and evaluated over trials. For each evaluation trial, unseen objects appear on the table and are randomly positioned. The results are shown in Tab. 5
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Spatial
Object
Goal
Long
Figure 7: The MOE gating score distribution histograms on the LIBERO benchmark of adaptive layer selection strategy.
Table 5: Real-world results with complex color, unseen clutter, diverse instruction challenges.
Method
Success Rate
w/o SF
22.5
w/ SF
37.5
H.2 COMPLEX SPATIAL CHALLENGES
As shown in Fig. "Transfer Block from Cardboard" task requires the robot transfer block from one cardboard box another. For this task, the highly constrained spatial positions require precise perception of the box openings and object locations. The results are shown in Tab. 6
Table 6: The real-world results on the Transfer Block from Cardbourd task.
Method
Success Rate
OLenVLA-OFT 3D-CAVLA SF (ours)
12.5
27.5
32.5
I RECONSTRUCTION PROBING
To further validate that the aligned VIAA visual tokens still contain non-spatial information (e.g.. color), we extend the depth probing experiment in our paper to color-reconstruccion probing ex- periment. Specifically, we freeze the whole VL.A model and train two-layer ML. and DPT head to simultancously predict patch-level color and depth. The probing visualizations are provided in Fig. [0] which further confirms the rich color information in the VLA visual tokens after the alignment.
J DOWNSTREAM TASK SUPERVISION STRATEGY
To understand what specific geometric properties are most dominant in these features, we design paradigm to directly align VLA with downstream properties of VGGT. Following the official implementation of VGGT, we mainly choose three geometric properties: depth, point maps, and dynamic tracks. Firstly, utilize the task-specific heads of VGGT VLA downstream heads.
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Training
Evaluation
Figure 8: Additional real-world experiments with complex color, unseen clutter, diverse instruction challenges.
Step1: pick
Step2: transfer
Step3 place
Figure 9: Additional ablation experiments for more complex spatial tasks.
Then, the visual tokens of VLA fed into the trainable VLA downstream heads to produce task- specific outputs (i.e., depth, point maps, or dynamic tracks). Finally, we treat the VGGT task-specific outputs ground-truth and calculate the loss between these two outputs. The overall pipeline is shown in the Fig.
K SCALING PERFORMANCE EVALUATION
To better evaluate our scaling performance on increasing data and training steps, we choose a dif- ficult task RoboTwin and collect additional data. The experiments are on the RoboTwin "beat block" task. The original data includes 50 episodes and we additionally collected 150 episodes, total of 200 episodes. The line charts of success rate comparison are shown Fig. T2
The x-axis of the original data-scaling curves is displayed in a logarithmic scale. Under the log scale, the curves visually appear much steeper. Therefore, as shown Fig. 13 we provide the data- sialasscaiing curves using the linear scale x-axis, which reflects the absolute dataset size without bias. In addition, to make sure that these lata-scaling curves fully converged, we conducted further experiments under the larger dataset sizes. Experimental results demonstrate that the dataset size increases, the performance gains gradually become marginal for both the baseline and our SF method. However, could find that under every setting of data sizes, SF consistently outperforms the baseline. In particular, even when the data-scaling curves nearly converge under large data sizes, our SF still maintains significantly higher success rate. This indicates that SF remains effective even under large data scales.
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Image
R Reconstruction
Figure Reconstruction probing results.
Together with token
actions
VLA visual taken
layer
VLA dewnstream hoad
VLA Downstream oulgut
images
Align
VGGT atlention layer
VGGT downstream head
VGGT
Latent features
Downstream output
Figure 11: The pipeline of the downstream task supervision strategy.
Training
Scaling
wro Aligrment wr Aligmment
wib Alignment Alggmmm:
300
30
Data Scaling
wuS Aligrment Aligmment
Wee Aligmment
Beat Block (easy)
Beat Block (hard)
Figure 12: Additional ablation experiments for training iteration and data with the logarithmic scale x-axis.
w/o Alignment Alignment
30.28
200
300
500
Figure 13: Data-scaling curves with linear scale x-axis.
22


Reviews:
[{"review_id": "nBJ9blxpPG", "reviewer": "Reviewer_A7M4", "summary": "The paper presents **Spatial Forcing (SF)**, a training-time regularizer that aligns intermediate visual embeddings of a VLA with geometry-rich representations from a pretrained 3D foundation model (VGGT). This auxiliary alignment loss injects privileged spatial priors into the model during fine-tuning, without requiring explicit 3D inputs or architectural modifications at inference time. Empirically, the authors evaluate SF on **LIBERO** and **RoboTwin 2.0** simulation benchmarks as well as several real-world manipulation tasks. SF consistently improves performance over both 2D and explicit-3D VLAs, achieving up to 3.8× faster convergence and demonstrating robustness to lighting, layout, and height variations in the physical setup.", "questions": "- In Table 1,the statistics of SpatialVLA, GeoVLA and 3D-CAVLA are same as the origin paper shows, do authors rigorously train different VLAs under same initialization and parameter?\n- In Table 2, VGGT without Position Embedding shows high success rates on two Libero benchmark, could you further analyze potential reason?\n- In Figure 4, the results are shown as success rates, but there are no explicit percentage labels or variance/error indicators (e.g., standard deviation or confidence intervals). Could the authors clarify whether these bars represent mean performance across seeds or single-run outcomes, and provide variance information to assess statistical significance?\n- Did you experiment with multi-layer or progressive alignment instead of a single-layer constraint?\n- Can SF improve other vision-driven policies (e.g., diffusion policy or world-model-based RL)?\n- Could feature alignment harm language following of base VLAs? Is there a trade-off between spatial and linguistic? Take pi0 experiment for example, can you ablation other task regression?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "> ### originality\n>\n> - Addresses the dependency of 3D VLAs on explicit geometric inputs such as depth maps or point clouds.\n> - Introduces a simple yet effective co-training mechanism that transfers 3D priors through a pretrained foundation model.\n>\n> - The privileged-representation intuition is intuitive, and the depth-probing visualization improves interpretability.\n>\n> ### Quality\n>\n> - The method is compatible with diverse VLA backbones and requires little engineering effort.\n> - The ablation studies on alignment layer and teacher model strengthen the empirical claims\n\n> ### Clarity\n>\n> - Presents results on LIBERO, RoboTwin 2.0, and real-robot experiments for a comprehensive evaluation.\n> - Figures and comparisons are well organized and easy to interpret.\n>\n> ### Significance\n>\n> - Bridging 2D vision-based manipulation with 3D perception is an important and rapidly growing research direction.\n> - SF provides a practical baseline and may inspire further work on representation alignment and spatial grounding", "weaknesses": "- The experiment setting is a bit simple. For simulation the improvement is marginal, while the real world tasks do not demonstrate enough spatial generation in initial pose distribution \n- The related work part lacks formal explanation for why representation-level cosine alignment leads to stable 3D awareness.\n- The supplemental video includes only few real world experiments, and the tasks are easy with jerky behavior. Please include both simulation and more real world experiments under different noise to prove robustness. \n- Currently, the alignment depth layer is chosen empirically, where authors could incorporate an adaptive strategy to improve robustness.", "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": "ffuJZlA2Le", "reviewer": "Reviewer_cbE1", "summary": "This paper addresses a critical challenge in Vision-Language-Action (VLA) models: the lack of inherent 3D spatial understanding, which stems from their foundation in Vision-Language Models (VLMs) trained on 2D data. The authors argue that existing solutions, which rely on explicit 3D sensor inputs (e.g., depth, point clouds) or 2D-to-3D estimators, are fraught with practical issues like sensor noise, hardware heterogeneity, data availability, and sub-optimal performance. To overcome this, they propose Spatial Forcing (SF), a novel and simple training strategy. Instead of modifying the model's input, SF implicitly instills spatial awareness by aligning the VLA's intermediate visual representations with geometric features extracted from a powerful, pretrained 3D foundation model (specifically, VGGT). This alignment is achieved via an auxiliary cosine similarity loss during the fine-tuning process. The authors demonstrate through extensive experiments in both simulation and the real world that this implicit supervision method not only achieves state-of-the-art performance, surpassing both 2D and explicit 3D VLA baselines, but also dramatically accelerates training convergence (up to 3.8x) and improves data efficiency.", "questions": "The paper argues that the aligned representations still preserve their \"original representational identity\" based on the t-SNE visualization. Could you elaborate on this? How do you ensure that the \"forcing\" process doesn't cause the visual features to discard important non-spatial information (e.g., texture, color) that might be crucial for other aspects of the task?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper tackles a fundamental and highly significant problem in embodied AI. The proposed SF method offers an elegant and practical paradigm that sidesteps the many real-world challenges associated with collecting and using explicit 3D data, making it a potentially high-impact contribution for the robotics community. \n\n- The core idea of using representation alignment to implicitly distill 3D knowledge from a foundation model into a VLA is novel and elegant.\n\n- The motivation is exceptionally well-established. The authors' use of a \"depth probing\" experiment is a simple yet powerful diagnostic tool that compellingly visualizes the problem—the lack of spatial information in standard VLA embeddings—and provides a clear justification for their approach.\n\n- The experimental design is thorough, validating the method across different base models (OpenVLA-OFT, $\\pi_0$), diverse simulation environments, and crucial real-world scenarios. The component-wise analysis is also well-executed, providing clear insights into the effects of different target representations and alignment layers.", "weaknesses": "- The success of Spatial Forcing is heavily contingent on the availability and quality of a powerful, pretrained 3D foundation model (VGGT in this case). This introduces a strong dependency, and the paper does not discuss the potential limitations if such a model is not available for a specific domain or embodiment, or how the performance of SF scales with the quality of this teacher model.\n\n- While the method is inference-free in terms of overhead, it introduces a non-trivial computational cost during training. It requires running a forward pass through the large VGGT model for every training sample to generate the target representations. This additional overhead in terms of computation and VRAM is not quantified or discussed, which is an important practical consideration that affects the overall \"efficiency\" claim.\n\n- The paper claims to outperform methods that use explicit 3D inputs (Table 1). While impressive, this comparison may not be entirely fair. SF is effectively distilling knowledge from a very powerful model (VGGT) that has already processed and structured 3D information. In contrast, methods using raw depth maps or point clouds must learn to interpret noisy, unstructured sensor data from scratch. The comparison is thus more akin to \"distilled 3D knowledge\" vs. \"raw 3D data.\"\n\n- The method uses the \"latent representation\" from VGGT as the supervision signal. This is somewhat of a black box. It would be beneficial to understand what specific geometric properties (e.g., relative depth, surface normals, object boundaries) are most dominant in these features and are being transferred to the VLA. An ablation using more interpretable outputs from VGGT (like its predicted depth map) as the target could provide valuable insight.", "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": "qgJXomHWt7", "reviewer": "Reviewer_giXr", "summary": "This submission employs intermediate-layer alignment to enhance spatial comprehension.Rather than utilizing explicit 3D inputs or a depth predictor in the VLA pipeline, this method only relies on VGGT during the training phase.Evaluations in both simulated and real-world environments demonstrate that the proposed method outperforms existing 3D VLAs.", "questions": "The main question concerns the effectiveness of the alignment. Given that data size and computational power will undoubtedly continue to grow in the future, will the proposed method still hold its effectiveness at that point?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Other 3D-aware VLA methods explicitly feed depth maps into the VLA model, whereas the proposed method does not require the input of depth maps during the inference phase—thus enabling faster training and inference. The method is simple and straightforward, and evaluations indeed show better performance compared to existing counterparts.", "weaknesses": "As Figure 5 illustrates, while the method with alignment improves the success rate more rapidly than the method without alignment, as the data volume and training iterations increase, the two methods achieve the same performance. This indicates that the proposed method does not remain valid as the data size grows. This raises a question: if the data volume or training steps continue to increase, could the method without alignment exhibit stronger performance instead?", "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": "bGss3e2BXo", "reviewer": "Reviewer_UKHb", "summary": "To address the limitations of VLA models largely relying on 2D visual features and the difficulty of acquiring and representing explicit 3D inputs, this paper proposes Spatial Forcing (SF), a method that aligns the deep features of VLA models with target features embedded with spatial awareness, thereby enhancing the spatial reasoning capability of VLA models during task execution and improving action prediction accuracy. The target features containing 3D spatial information are provided by the VGGT model. \n\nThe authors investigate the importance of 3D spatial perception for the success rate of VLA tasks and validate the effectiveness of the SF method in enhancing 2D-based VLA models through multiple simulators and real-world experiments. Additionally, various visualization techniques demonstrate that the VLA model’s features are focused on spatial information and object geometry representation.", "questions": "In Table 2, the first row represents a model without target representation and without feature alignment, which uses OpenVLA-OFT as the base model. However, the reported performance in Table 2 does not match the results in Table 1, which may affect the validity of the ablation study. It would be helpful if the authors could explain the cause of this discrepancy or provide additional experimental details.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.\tAnalysis of 3D spatial perception limitations in existing VLA methods: The paper identifies that most current VLA approaches lack understanding of 3D spatial information and highlights the limitations of existing attempts to incorporate 3D cues into VLA models.\n2.\tDeep insights into LLM feature layers: The authors investigate the features from different layers of the LLM and determine appropriate layers for optimal feature alignment.\n3.\tEfficient feature alignment with SF: SF is a training-only feature alignment method that significantly improves data efficiency and model capability, particularly in real-world scenarios.", "weaknesses": "1.\tWhile SF demonstrates impressive capability in obtaining spatially informed representations, aligning VGGT features with deep layers of the LLM (e.g., the 24th layer) might potentially compromise the 2D image-derived features acquired in the preceding layers, such as semantic understanding and generalization ability. The authors could provide experimental verification, for example, through real-world experiments testing scene generalization or tasks requiring strong semantic comprehension.\n2.\tIn the real-world experiments, there is no baseline model that explicitly takes 3D inputs. Given that SF emphasizes comparison with 3D-aware models in both method and simulation experiments, including an explicit 3D-input baseline in more spatially complex real-world scenarios could more convincingly demonstrate the advantages of SF.", "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)