# SpaRE: Enhancing Spatial Reasoning in Vision-Language Models with Synthetic Data

arXiv: https://arxiv.org/abs/2504.20648 · HTML: https://mlsanity.com/paper/2504.20648/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- Synthetic data and self-improvement for LLMs / [Self-improving MLLMs and synthetic data](https://mlsanity.com/synth/self-improving-mllms-and-synthetic-data/)

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://mlsanity.com/paper/2409.17146/) (2024)
- [3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark](https://mlsanity.com/paper/2412.07825/) (2024)
- [Benchmarking Spatial Relationships in Text-to-Image Generation](https://mlsanity.com/paper/2212.10015/) (2022)
- [Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities](https://mlsanity.com/paper/2410.17385/) (2024)
- [STUPD: A Synthetic Dataset for Spatial and Temporal Relation Reasoning](https://mlsanity.com/paper/2309.06680/) (2023)

## Built on by (most cited first, 21 of 21)

- [How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspective](https://mlsanity.com/paper/2509.18905/) (2025)
- [Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks](https://mlsanity.com/paper/2510.25760/) (2025)
- [Spatial Reasoning in Multimodal Large Language Models: A Survey of Tasks, Benchmarks and Methods](https://mlsanity.com/paper/2511.15722/) (2025)
- [SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards](https://mlsanity.com/paper/2511.07403/) (2025)
- [From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models](https://mlsanity.com/paper/2605.20177/) (2026)
- [World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning](https://mlsanity.com/paper/2604.26934/) (2026)
- [RAU: Reference-based Anatomical Understanding with Vision Language Models](https://mlsanity.com/paper/2509.22404/) (2025)
- [GRAID: Enhancing Spatial Reasoning of VLMs Through High-Fidelity Data Generation](https://mlsanity.com/paper/2510.22118/) (2025)
- [Reasoning Path and Latent State Analysis for Multi-view Visual Spatial Reasoning: A Cognitive Science Perspective](https://mlsanity.com/paper/2512.02340/) (2025)
- [MosaicThinker: On-Device Visual Spatial Reasoning for Embodied AI via Iterative Construction of Space Representation](https://mlsanity.com/paper/2602.07082/) (2026)
- [Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning](https://mlsanity.com/paper/2606.11683/) (2026)
- [PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning](https://mlsanity.com/paper/2605.19382/) (2026)
- [SpatialTraceGen: High-Fidelity Traces for Efficient VLM Spatial Reasoning Distillation](https://mlsanity.com/paper/2511.00054/) (2025)
- [The N-Body Problem: Parallel Execution from Single-Person Egocentric Video](https://mlsanity.com/paper/2512.11393/) (2025)
- [From Steering to Pedalling: Do Autonomous Driving VLMs Generalize to Cyclist-Assistive Spatial Perception and Planning?](https://mlsanity.com/paper/2602.10771/) (2026)
- [Learning to Draw ASCII Improves Spatial Reasoning in Language Models](https://mlsanity.com/paper/2604.14641/) (2026)
- [ReRef-3D: A Benchmark for Spatial Referring Expression-Guided 3D Scene Rearrangement](https://mlsanity.com/paper/2608.16011/) (2026)
- [MVVBench: Benchmarking 4D Reasoning in Vision-Language Models](https://mlsanity.com/paper/2609.30952/) (2026)
- [GaugeVLM: Structuring Spatial Supervision with Measured Geometric Interventions](https://mlsanity.com/paper/2609.38285/) (2026)
- [GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models](https://mlsanity.com/paper/2610.18694/) (2026)
- [Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2610.19108/) (2026)

## Abstract (arXiv)

Vision-language models (VLMs) work well in tasks ranging from image captioning to visual question answering (VQA), yet they struggle with spatial reasoning, a key skill for understanding our physical world that humans excel at. We find that spatial relations are generally rare in widely used VL datasets, with only a few being well represented, while most form a long tail of underrepresented relations. This gap leaves VLMs ill-equipped to handle diverse spatial relationships. To bridge it, we construct a synthetic VQA dataset focused on spatial reasoning generated from hyper-detailed image descriptions in Localized Narratives, DOCCI, and PixMo-Cap. Our dataset consists of 455k samples containing 3.4 million QA pairs. Trained on this dataset, our Spatial-Reasoning Enhanced (SpaRE) VLMs show strong improvements on spatial reasoning benchmarks, achieving up to a 49% performance gain on the What's Up benchmark, while maintaining strong results on general tasks. Our work narrows the gap between human and VLM spatial reasoning and makes VLMs more capable in real-world tasks such as robotics and navigation.