STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs
Multimodal Large Language Models (MLLMs) have demonstrated remarkable
capabilities across diverse tasks, yet they lag significantly behind humans in
spatial reasoning. We investigate this gap through Transformation-Driven Visual
Reasoning (TVR), a challenging task requiring identification of object
transformations across images under varying viewpoints. While traditional
Supervised Fine-Tuning (SFT) fails to generate coherent reasoning paths in
cross-view settings, sparse-reward Reinforcement Learning (RL) suffers from
inefficient exploration and slow convergence. To address these limitations, we
propose STAR-R1, a novel framework that integrates a single-stage RL paradigm
with a fine-grained reward mechanism tailored for TVR. Specifically, STAR-R1
rewards partial correctness while penalizing excessive enumeration and passive
inaction, enabling efficient exploration and precise reasoning. Comprehensive
evaluations demonstrate that STAR-R1 achieves state-of-the-art performance
across all 11 metrics, outperforming SFT by 23% in cross-view scenarios.
Further analysis reveals STAR-R1's anthropomorphic behavior and highlights its
unique ability to compare all objects for improving spatial reasoning. Our work
provides critical insights in advancing the research of MLLMs and reasoning
models. The codes, model weights, and data will be publicly available at
https://github.com/zongzhao23/STAR-R1.
6835adccc9704b79b7f18dd0
Zongzhao Li
admin_assigned
2025-05-27T12:49:37.146Z
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lzz
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lizongzhao
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Zongyang Ma
admin_assigned
2025-05-27T12:49:26.103Z
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Zongyang Ma
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zyma
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Mingze Li
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Songyou Li
admin_assigned
2025-05-27T12:49:52.377Z
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Songyou Li
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Wthinker
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Yu Rong
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2025-06-10T09:29:51.963Z
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Yu Rong
user
Swrooy
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Tingyang Xu
admin_assigned
2025-05-27T12:49:58.919Z
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Tingyang Xu
user
xuty007
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Ziqi Zhang
6835adccc9704b79b7f18dd7
Deli Zhao
6835adccc9704b79b7f18dd8
Wenbing Huang
STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs
Multimodal Large Language Models (MLLMs) have demonstrated remarkable
capabilities across diverse tasks, yet they lag significantly behind humans in
spatial reasoning. We investigate this gap through Transformation-Driven Visual
Reasoning (TVR), a challenging task requiring identification of object
transformations across images under varying viewpoints. While traditional
Supervised Fine-Tuning (SFT) fails to generate coherent reasoning paths in
cross-view settings, sparse-reward Reinforcement Learning (RL) suffers from
inefficient exploration and slow convergence. To address these limitations, we
propose STAR-R1, a novel framework that integrates a single-stage RL paradigm
with a fine-grained reward mechanism tailored for TVR. Specifically, STAR-R1
rewards partial correctness while penalizing excessive enumeration and passive
inaction, enabling efficient exploration and precise reasoning. Comprehensive
evaluations demonstrate that STAR-R1 achieves state-of-the-art performance
across all 11 metrics, outperforming SFT by 23% in cross-view scenarios.
Further analysis reveals STAR-R1's anthropomorphic behavior and highlights its
unique ability to compare all objects for improving spatial reasoning. Our work
provides critical insights in advancing the research of MLLMs and reasoning
models. The codes, model weights, and data will be publicly available at
https://github.com/zongzhao23/STAR-R1.
STAR-R1, a novel RL framework with a fine-grained reward mechanism, enhances spatial reasoning in multimodal large language models by addressing limitations in traditional SFT and sparse-reward RL.
Transformation-Driven Visual Reasoning Supervised Fine-Tuning Reinforcement Learning single-stage RL fine-grained reward mechanism partial correctness excessive enumeration spatial reasoning multimodal large language models