UniCAR-RL: Seeing Better before Thinking Deeper in Visual Mathematics

• URL: http://arxiv.org/abs/2609.13849v1
• Date: Sat, 12 Sep 2026 10:07:04 GMT
• Title: UniCAR-RL: Seeing Better before Thinking Deeper in Visual Mathematics
• Abstract summary: We propose UniCAR-RL, an annotation-free reinforcement learning framework.<n>By explicitly decoupling the optimization of perception and reasoning during the training process, it achieves isolation and optimization of both capabilities.<n> Experiments show that UniCAR-RL substantially improves MLLMs' mathematical and visual reasoning using only raw short-answer data.
• Score: 33.01988707981005
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) often struggle with complex mathematical visual reasoning primarily due to a lack of fine-grained perception, causing initial visual hallucinations to directly trigger cascading reasoning failures. In traditional end-to-end reinforcement learning (RL), sparse rewards fail to decouple perceptual hallucinations from logical missteps, hindering targeted perception optimization. Alternatively, fine-tuning with perception-enhanced CoT data incurs high costs and hallucinations. In this paper, we address these challenges by proposing UniCAR-RL, an annotation-free RL framework. By explicitly decoupling the optimization of perception and reasoning during the training process, it achieves isolation and optimization of both capabilities. Specifically, UniCAR-RL consists of three synergistic branches: 1) a Caption-RL branch that optimizes perception capabilities through verifier-guided reasoning validation; 2) a Reasoning-RL branch that performs logical reasoning based on a gold image description to halt cascading errors; 3) a QA-RL branch that retains native end-to-end alignment to ensure robust question-answering performance. Experiments show that UniCAR-RL substantially improves MLLMs' mathematical and visual reasoning using only raw short-answer data. Furthermore, it demonstrates strong generalization across diverse architectures and scales.

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