Multimodal Mathematical Reasoning with Diverse Solving Perspective
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
Recent progress in large-scale reinforcement learning (RL) has notably
enhanced the reasoning capabilities of large language models (LLMs), especially
in mathematical domains. However, current multimodal LLMs (MLLMs) for
mathematical reasoning often rely on one-to-one image-text pairs and
single-solution supervision, overlooking the diversity of valid reasoning
perspectives and internal reflections. In this work, we introduce MathV-DP, a
novel dataset that captures multiple diverse solution trajectories for each
image-question pair, fostering richer reasoning supervision. We further propose
Qwen-VL-DP, a model built upon Qwen-VL, fine-tuned with supervised learning and
enhanced via group relative policy optimization (GRPO), a rule-based RL
approach that integrates correctness discrimination and diversity-aware reward
functions. Our method emphasizes learning from varied reasoning perspectives
and distinguishing between correct yet distinct solutions. Extensive
experiments on the MathVista's minitest and Math-V benchmarks demonstrate that
Qwen-VL-DP significantly outperforms prior base MLLMs in both accuracy and
generative diversity, highlighting the importance of incorporating diverse
perspectives and reflective reasoning in multimodal mathematical reasoning.
