GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric
  Reasoning
Recent advancements in reinforcement learning (RL) have enhanced the
reasoning abilities of large language models (LLMs), yet the impact on
multimodal LLMs (MLLMs) is limited. Particularly in vision-intensive tasks like
geometric reasoning, MLLMs hallucinate frequently, leading to inaccurate
reasoning. We attribute this to the perceptual bottleneck in MLLMs, which caps
the benefits of reasoning training. To quantify this, we design a
Geo-Perception Question-Answering (GeoPQA) benchmark, targeting basic geometric
concepts and spatial relationships. Experiments on GeoPQA reveal significant
shortcomings of MLLMs in visual perception, which constrain RL reward signals
for effective training. To address this bottleneck, we propose a two-stage RL
training framework by first enhancing the visual perception of geometric
structures, then fostering reasoning capabilities. Applied to
Qwen2.5-VL-3B-Instruct, our two-stage training improves geometric reasoning by
9.7% and geometric problem solving by 9.1%, compared to the direct reasoning
training approach. Our method also generalizes to other vision-intensive
domains like figure understanding, highlighting the importance of perceptual
grounding in effective MLLM reasoning.
68d22fc51ca7156988a8ee0f
Guizhen Chen
claimed_verified
2025-09-23T10:05:26.302Z
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/avatars/fdbe121535dea940edd2766161393485.svg
Chen
user
Guizhen
68d22fc51ca7156988a8ee10
Weiwen Xu
68d22fc51ca7156988a8ee11
Hao Zhang
claimed_verified
2025-09-23T10:05:22.981Z
64b7cd74ff6d81ae297feded
/avatars/880fbc96cc093f5e901ce84f32a1d21d.svg
ZHANG HAO
user
26hzhang
68d22fc51ca7156988a8ee12
Hou Pong Chan
68d22fc51ca7156988a8ee13
Deli Zhao
68d22fc51ca7156988a8ee14
Anh Tuan Luu
68d22fc51ca7156988a8ee15
Yu Rong
claimed_verified
2025-09-23T10:05:12.297Z
642eecbf9b2484d7d8526781
https://cdn-avatars.huggingface.co/v1/production/uploads/642eecbf9b2484d7d8526781/4IvGbd66s49Wx5pZyZGHA.png
Yu Rong
user
Swrooy
GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric
  Reasoning
Recent advancements in reinforcement learning (RL) have enhanced the
reasoning abilities of large language models (LLMs), yet the impact on
multimodal LLMs (MLLMs) is limited. Particularly in vision-intensive tasks like
geometric reasoning, MLLMs hallucinate frequently, leading to inaccurate
reasoning. We attribute this to the perceptual bottleneck in MLLMs, which caps
the benefits of reasoning training. To quantify this, we design a
Geo-Perception Question-Answering (GeoPQA) benchmark, targeting basic geometric
concepts and spatial relationships. Experiments on GeoPQA reveal significant
shortcomings of MLLMs in visual perception, which constrain RL reward signals
for effective training. To address this bottleneck, we propose a two-stage RL
training framework by first enhancing the visual perception of geometric
structures, then fostering reasoning capabilities. Applied to
Qwen2.5-VL-3B-Instruct, our two-stage training improves geometric reasoning by
9.7% and geometric problem solving by 9.1%, compared to the direct reasoning
training approach. Our method also generalizes to other vision-intensive
domains like figure understanding, highlighting the importance of perceptual
grounding in effective MLLM reasoning.
A two-stage reinforcement learning framework improves geometric reasoning and problem-solving in multimodal language models by first enhancing visual perception.
reinforcement learning large language models multimodal language models geometric reasoning hallucination perceptual bottleneck Geo-Perception Question-Answering visual perception reasoning capabilities Qwen2.5-VL-3B-Instruct figure understanding perceptual grounding