G eo PQA : Bridging the Visual Perception Gap in MLLM s for Geometric Reasoning (2025)

Abstract: 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, constraining RL reward signals for 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 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.

## introduction recent advances in reasoning models such as deepseek-r1 (deepseek-ai, 2025) have demonstrated that reinforcement learning with verifiable reward (rlvr) can markedly strengthen the reasoning abilities of large language models (llms; @xcite ; @xcite ). motivated by these successes, several studies have applied similar rl training recipes to multimodal llms (mllms; @xcite @xcite @xcite as shown in the figure, in △abc, ∠@xmath0, rotate △abc counterclockwise 60° around point a to obtain △aed. ad and bc intersect at point f, then the degree of ∠afc is () choices: a. 84° b. 80° c. 60° d. 90°s ince △aed is obtained by rotating △abc counterclockwise around point a by 60°, we have ∠@xmath1. therefore, ∠@xmath2. therefore, the correct answer is: b. 80°. the answer is: b. et al. 2025; @xcite . however, the performance gains on vision-intensive reasoning benchmarks, such as mathverse @xcite and mathvista @xcite , remain relatively limited. a closer examination suggests that these limitations often originate from more foundational issues in visual understanding, even before complex reasoning is attempted. figure 1 shows an example of a model struggling with identifying the rotation angle -a task that is easy for humans. such fundamental errors in vision understanding affect subsequent logical deductions, preventing the model from being rewarded. ## methodology in this section, we first systematically assess the perceptual capabilities of mllms in the geometric domain. next, we develop a two-stage rl framework that first enhances perception and subsequently boosts reasoning capabilities of mllms. ## results and analyses main results. in addition, we observe that incorporating perception data, whether mixed or sequential, boosts reasoning performance over reasoning-only rl. this validates the effectiveness of our perception qa dataset. moreover, a structured, sequential approach of perception followed by reasoning training yields greater benefits than simply mixing perception and reasoning data, validating the effectiveness of our two-stage framework. compared to other leading open-source mllms and models specialized in mathematics, our method establishes new state-of-the-art performance on geometric tasks, even with a much smaller model size. while gpt-4o remains the top performer among proprietary models with 74.1% on gr and 75.0% on gps, our two-stage framework, applied to the qwen2.5-vl-3b-instruct, considerably narrows the performance gap to just around 2%. enhancement on visual perception. model geopqa qwen2.5-vl-3b-instruct 68.2 w/ perception 89.8 w/ reasoning 53.1 w/ perception followed by reasoning 83.2 ## conclusion we investigate the fundamental challenge of improving geometric reasoning capabilities in mllms. our analysis reveals that the effectiveness of reinforcement learning for reasoning is significantly constrained by mllms' visual perception, a critical bottleneck that is often not directly measured in previous work. by developing a targeted assessment of geometric perception and introducing a two-stage rl framework that explicitly enhances visual perception prior to reasoning training, we achieved substantial improvements on challenging benchmarks. the success of our perception-first training approach highlights an important principle for future work in multimodal reasoning: strong perceptual foundations are prerequisites for effective higher-level reasoning. future directions include exploring whether our approach can enhance the performance of recent thinking-with-images approaches @xcite @xcite and generalizing our framework to other vision-intensive tasks like chart understanding @xcite .