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 # **Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs**
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 ## 💡 Introduction
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 **Yifan Shen, Yuanzhe Liu, Jingyuan Zhu, Xu Cao, Xiaofeng Zhang, Yixiao He, Wenming Ye, James Matthew Rehg, Ismini Lourentzou**
 
-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 novel VLM designed to address these limitations. First, we propose Multi-LLM Guided Monte Carlo Tree Search (M3CTS) and Fine-Grained Spatial Rewards methods to construct a high-quality dataset. Second, we use fine-grained Direct Preference Optimization (fDPO) to train our model. fDPO introduces segment-specific preference granularity for descriptive grounding and logical reasoning, achieving an average improvement of 4.1% over standard DPO across spatial quality tasks, and a 9.0% boost in spatial quantity tasks. To address the scarcity of multi-step spatial reasoning data, M3CTS enables collaborative exploration of diverse reasoning paths, significantly enriching spatial comprehension and logical coherence. Empirical evaluations demonstrate that SpatialReasoner-R1 sets a new state-of-the-art 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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-## Model Card Contact
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-[email protected]
+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 novel VLM designed to address these limitations. First, we propose Multi-LLM Guided Monte Carlo Tree Search (M3CTS) and Fine-Grained Spatial Rewards methods to construct a high-quality dataset. Second, we use fine-grained Direct Preference Optimization (fDPO) to train our model. fDPO introduces segment-specific preference granularity for descriptive grounding and logical reasoning, achieving an average improvement of 4.1% over standard DPO across spatial quality tasks, and a 9.0% boost in spatial quantity tasks. To address the scarcity of multi-step spatial reasoning data, M3CTS enables collaborative exploration of diverse reasoning paths, significantly enriching spatial comprehension and logical coherence. Empirical evaluations demonstrate that SpatialReasoner-R1 sets a new state-of-the-art on SpatialRGPT-Bench, outperforming the strongest baseline by 9.8% in average accuracy, while maintaining competitive performance on general vision-language tasks.