Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs

• URL: http://arxiv.org/abs/2506.21656v1
• Date: Thu, 26 Jun 2025 18:00:00 GMT
• Title: Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs
• Authors: Yifan Shen, Yuanzhe Liu, Jingyuan Zhu, Xu Cao, Xiaofeng Zhang, Yixiao He, Wenming Ye, James Matthew Rehg, Ismini Lourentzou,
• Abstract summary: Current vision-language models struggle with fine-grained spatial reasoning.<n>We introduce SpatialReasoner-R1, a vision-language reasoning model designed to address these limitations.<n>We show that fDPO achieves an average improvement of 4.1% over standard DPO across spatial quality tasks, and a 9.0% gain in spatial quantity tasks.
• Score: 12.883053399582174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current Vision-Language Models (VLMs) struggle with fine-grained spatial reasoning, particularly when multi-step logic and precise spatial alignment are required. In this work, we introduce SpatialReasoner-R1, a vision-language reasoning model designed to address these limitations. To construct high-quality supervision for spatial reasoning, we design a Multi-Model Monte Carlo Tree Search (M3CTS) method that generates diverse, logically consistent Long Chain-of-Thought (LongCoT) reasoning trajectories. In addition, we propose fine-grained Direct Preference Optimization (fDPO), which introduces segment-specific preference granularity for descriptive grounding and logical reasoning, guided by a spatial reward mechanism that evaluates candidate responses based on visual consistency, spatial grounding, and logical coherence. Experimental results demonstrate that fDPO achieves an average improvement of 4.1% over standard DPO across spatial quality tasks, and a 9.0% gain in spatial quantity tasks. SpatialReasoner-R1, trained with fDPO, sets a new SoTA on SPATIALRGPT-Bench, outperforming the strongest baseline by 9.8% in average accuracy, while maintaining competitive performance on general vision-language tasks.

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