GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric
  Reasoning
Authors: name: Guizhen Chen, name: Weiwen Xu, name: Hao Zhang, name: Hou Pong Chan, name: Deli Zhao, name: Anh Tuan Luu, name: Yu Rong

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.