SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and
  Verifiable Rewards
DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text
domain through stable reinforcement learning (RL). Recently, in the multimodal
domain, works have begun to directly apply RL to generate R1-like free-form
reasoning for Visual Question Answering (VQA) tasks. However, multimodal tasks
share an intrinsically different nature from textual tasks, which heavily rely
on the understanding of the input image to solve the problem. Therefore, such
free-form reasoning faces two critical limitations in the VQA task: (1)
Extended reasoning chains diffuse visual focus away from task-critical regions,
degrading answer accuracy. (2) Unverifiable intermediate steps amplify
policy-gradient variance and computational costs overhead. To address these
issues, in this paper, we introduce SATORI (Spatially
Anchored Task Optimization with
ReInforcement Learning), which decomposes VQA into three
verifiable stages, including global image captioning, region localization, and
answer prediction, each supplying explicit reward signals. Furthermore, we also
introduce VQA-Verify, a 12k dataset annotated with answer-aligned captions and
bounding-boxes to facilitate training. Experiments demonstrate consistent
performance improvements across seven VQA benchmarks, achieving up to 15.7%
improvement in accuracy in accuracy compared to the R1-like baseline. Our
analysis of the attention map confirms enhanced focus on critical regions,
which brings improvements in accuracy. Our code is available at
https://github.com/justairr/SATORI-R1.
683524e492151d8beb27f7eb
Chuming Shen
admin_assigned
2025-05-27T14:44:01.542Z
64afb1759f6af3d885e79596
https://cdn-avatars.huggingface.co/v1/production/uploads/64afb1759f6af3d885e79596/U0C_zSEhpY4HKRZbL3EYr.png
Chuming Shen
user
justairr
683524e492151d8beb27f7ec
Wei Wei
683524e492151d8beb27f7ed
Xiaoye Qu
683524e492151d8beb27f7ee
Yu Cheng
claimed_verified
2025-06-02T07:49:07.836Z
67017abfe4d49b157ac534d9
/avatars/997e1b9f54b27a7728a9d4abfee4ba91.svg
Yu Cheng
user
ych133
SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and
  Verifiable Rewards
DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text
domain through stable reinforcement learning (RL). Recently, in the multimodal
domain, works have begun to directly apply RL to generate R1-like free-form
reasoning for Visual Question Answering (VQA) tasks. However, multimodal tasks
share an intrinsically different nature from textual tasks, which heavily rely
on the understanding of the input image to solve the problem. Therefore, such
free-form reasoning faces two critical limitations in the VQA task: (1)
Extended reasoning chains diffuse visual focus away from task-critical regions,
degrading answer accuracy. (2) Unverifiable intermediate steps amplify
policy-gradient variance and computational costs overhead. To address these
issues, in this paper, we introduce SATORI (Spatially
Anchored Task Optimization with
ReInforcement Learning), which decomposes VQA into three
verifiable stages, including global image captioning, region localization, and
answer prediction, each supplying explicit reward signals. Furthermore, we also
introduce VQA-Verify, a 12k dataset annotated with answer-aligned captions and
bounding-boxes to facilitate training. Experiments demonstrate consistent
performance improvements across seven VQA benchmarks, achieving up to 15.7%
improvement in accuracy in accuracy compared to the R1-like baseline. Our
analysis of the attention map confirms enhanced focus on critical regions,
which brings improvements in accuracy. Our code is available at
https://github.com/justairr/SATORI-R1.
SATORI decomposes VQA into verifiable stages with explicit rewards to enhance focus on critical regions and reduce policy-gradient variance, achieving significant performance improvements.
stable reinforcement learning multimodal domain Visual Question Answering (VQA) extended reasoning chains policy-gradient variance global image captioning region localization answer prediction attention map VQA-Verify