SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and
  Verifiable Rewards
Authors: name: Chuming Shen, name: Wei Wei, name: Xiaoye Qu, name: Yu Cheng

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.