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Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Vision-language-action ModelRepresentation Learning
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.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Spatial Forcing, an alignment strategy that guides VLA models to develop spatial comprehension by aligning intermediate visual embeddings with pretrained 3D foundation models. It resides in the 'Foundation Model Alignment for Spatial Encoding' leaf, which contains four papers total. This leaf sits within the broader 'Implicit Spatial Representation Learning' branch, indicating a moderately populated research direction focused on learning spatial understanding without explicit 3D sensors. The taxonomy shows this is one of four distinct approaches to implicit spatial learning, suggesting a developing but not yet saturated area.

The taxonomy reveals neighboring leaves exploring alternative implicit strategies: 'Occupancy-Based Implicit Supervision' uses 3D occupancy signals, 'Gaussian-Based Spatial Representations' employs Gaussian primitives, and 'Neural Implicit Spatial Fields' leverages continuous neural encodings. These sibling directions share the goal of avoiding explicit depth sensors but differ in their geometric priors. The broader taxonomy also includes 'Explicit 3D Integration' branches that directly incorporate depth maps or point clouds, and 'Reasoning and Action Alignment' branches emphasizing step-by-step spatial reasoning. Spatial Forcing diverges from explicit methods by operating purely through latent alignment, and from reasoning-focused work by targeting representation learning rather than inference-time verification.

Among thirty candidates examined, none clearly refute the three core contributions. The 'Spatial Forcing alignment strategy' examined ten candidates with zero refutable overlaps; the 'depth probing analysis' similarly found no prior work among ten candidates; and the 'training/data efficiency improvements' showed the same pattern across ten candidates. This suggests that within the limited search scope, the specific combination of intermediate-layer alignment with 3D foundation models and the accompanying depth probing methodology appear relatively unexplored. However, the search scale is modest, and the taxonomy shows three sibling papers in the same leaf, indicating related alignment-based approaches exist in close proximity.

Based on the top-thirty semantic matches and taxonomy structure, the work appears to occupy a distinct position within an active but not overcrowded research direction. The absence of refutable candidates across all contributions suggests novelty within the examined scope, though the presence of sibling papers and neighboring implicit spatial learning methods indicates the broader conceptual space is being explored. The analysis covers alignment-focused implicit methods but does not exhaustively survey all spatial reasoning or explicit 3D integration approaches in the field.

Taxonomy

Core-task Taxonomy Papers
22
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Enhancing spatial awareness in vision-language-action models through implicit representation alignment. The field addresses how embodied agents can better understand and act upon spatial information by bridging vision, language, and action modalities. The taxonomy reveals several complementary directions: Implicit Spatial Representation Learning focuses on learning spatial encodings without explicit 3D reconstruction, often aligning foundation models with geometric cues; Explicit 3D Integration and Reconstruction builds structured scene representations using methods like neural radiance fields or Gaussian splatting; Reasoning and Action Alignment emphasizes grounding language instructions in spatial contexts and translating them into executable actions; Adaptation and Alignment Strategies explore techniques for fine-tuning or steering pretrained models toward spatial tasks; and Efficient Architectures and World Modeling investigates scalable designs and predictive models for embodied intelligence. Representative works span from implicit alignment approaches like Occvla[1] and Flowvla[2] to explicit geometric methods such as VL-Fields[7], while surveys like Foundation Models Robotic Survey[6] and World Models VLA Survey[15] provide broader context. A particularly active line of work centers on implicit alignment strategies that avoid costly 3D reconstruction while still capturing spatial structure. Spatial Forcing[0] sits within this branch, specifically targeting foundation model alignment for spatial encoding. It shares thematic ground with GeoAware-VLA[14] and Spatial to Actions[18], which similarly emphasize spatial grounding without explicit geometry, but differs in its focus on implicit representation alignment rather than direct geometric awareness or action translation. Meanwhile, methods like GAIR[3] and Evo-0[4] explore alternative alignment and adaptation pathways, highlighting trade-offs between representational richness and computational efficiency. The central tension across these branches involves balancing the expressiveness of spatial representations—whether implicit features, explicit 3D models, or hybrid approaches—against the practical demands of real-time robotic control and generalization to novel environments.

Claimed Contributions

Spatial Forcing alignment strategy for VLA models

The authors propose Spatial Forcing (SF), an alignment method that supervises intermediate visual embeddings of vision-language-action models using geometric representations from pretrained 3D foundation models. This approach enables VLAs to develop spatial comprehension capabilities without requiring explicit 3D sensor inputs or depth estimators.

10 retrieved papers
Depth probing analysis revealing spatial insufficiency in VLA embeddings

The authors conduct a depth probing experiment that demonstrates visual embeddings learned solely from 2D images in current VLA models fail to produce meaningful spatial structures. This observation motivates their proposed alignment strategy to address the spatial reasoning gap.

10 retrieved papers
Demonstration of training efficiency and data efficiency improvements

The authors show through extensive experiments that their Spatial Forcing method achieves state-of-the-art results while accelerating training by up to 3.8× and improving data efficiency, requiring significantly less data to achieve comparable performance across simulation and real-world robotic tasks.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Spatial Forcing alignment strategy for VLA models

The authors propose Spatial Forcing (SF), an alignment method that supervises intermediate visual embeddings of vision-language-action models using geometric representations from pretrained 3D foundation models. This approach enables VLAs to develop spatial comprehension capabilities without requiring explicit 3D sensor inputs or depth estimators.

Contribution

Depth probing analysis revealing spatial insufficiency in VLA embeddings

The authors conduct a depth probing experiment that demonstrates visual embeddings learned solely from 2D images in current VLA models fail to produce meaningful spatial structures. This observation motivates their proposed alignment strategy to address the spatial reasoning gap.

Contribution

Demonstration of training efficiency and data efficiency improvements

The authors show through extensive experiments that their Spatial Forcing method achieves state-of-the-art results while accelerating training by up to 3.8× and improving data efficiency, requiring significantly less data to achieve comparable performance across simulation and real-world robotic tasks.

Table of Contents