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arXiv · 2511.00054

SpatialTraceGen: High-Fidelity Traces for Efficient VLM Spatial Reasoning Distillation

Gio Huh·Dhruv Sheth·Rayhan Zirvi·Frank Xiao
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Abstract

While Vision-Language Models (VLMs) excel in many areas, they struggle with complex spatial reasoning, which requires problem decomposition and strategic tool use. Fine-tuning smaller, more deployable models offers an efficient path to strong performance, but this is hampered by a major bottleneck: the absence of high-quality, step-by-step reasoning data. To address this data-efficiency gap, we introduce SpatialTraceGen, a framework to distill the reasoning processes of a large teacher model into a high-quality dataset of multi-hop, multi-tool reasoning traces. A key innovation is our automated Verifier, which scalably ensures the fidelity of each reasoning step, providing a cost-effective alternative to manual human annotation. On the CLEVR-Humans benchmark, this verifier-guided process improves the average quality score of traces by 17\% while reducing quality variance by over 40\%. SpatialTraceGen delivers a dataset of expert traces, providing the structured, step-by-step examples of tool use necessary for effective fine-tuning and sample-efficient offline reinforcement learning.

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Gio Huh, Dhruv Sheth, Rayhan Zirvi, Frank Xiao. 2025-10-28. SpatialTraceGen: High-Fidelity Traces for Efficient VLM Spatial Reasoning Distillation. https://arxiv.org/abs/2511.00054

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