Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs
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.
6861f2b89e7509383d29ab35
Yifan Shen
claimed_verified
2025-07-04T07:50:52.209Z
65e387095132c2edd193ae49
/avatars/39278e5b026bcbdde88c560fc54018c5.svg
Yifan Shen
user
SivanSX
6861f2b89e7509383d29ab36
Yuanzhe Liu
6861f2b89e7509383d29ab37
Jingyuan Zhu
6861f2b89e7509383d29ab38
Xu Cao
claimed_verified
2025-07-01T10:14:30.129Z
5fc9f05d52770aca770bd3d9
https://cdn-avatars.huggingface.co/v1/production/uploads/5fc9f05d52770aca770bd3d9/axiKCXPYttXEWC-RqCSfh.jpeg
Xu Cao
user
IrohXu
6861f2b89e7509383d29ab39
Xiaofeng Zhang
6861f2b89e7509383d29ab3a
Yixiao He
6861f2b89e7509383d29ab3b
Wenming Ye
6861f2b89e7509383d29ab3c
James Matthew Rehg
6861f2b89e7509383d29ab3d
Ismini Lourentzou
claimed_verified
2025-07-05T07:53:57.240Z
678ac3b31cbaa0b4bc295885
/avatars/1244fc1b305c9c6383df9bb5e4707347.svg
Ismini Lourentzou
user
isminoula
Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs
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.
SpatialReasoner-R1, a vision-language reasoning model, uses Multi-Model Monte Carlo Tree Search and fine-grained Direct Preference Optimization to improve spatial reasoning, setting a new state-of-the-art on SPATIALRGPT-Bench.
vision-language models SpatialReasoner-R1 Multi-Model Monte Carlo Tree Search M3CTS Long Chain-of-Thought LongCoT fine-grained Direct Preference Optimization fDPO segment-specific preference granularity descriptive grounding logical reasoning spatial reward mechanism visual consistency spatial grounding logical coherence SPATIALRGPT-Bench