Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training

• URL: http://arxiv.org/abs/2605.11931v1
• Date: Tue, 12 May 2026 10:44:35 GMT
• Title: Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training
• Abstract summary: Post-training with explicit reasoning traces is common to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs)<n>We propose VISTA, a vision-aware self-improvement training framework for enhancing the multimodal reasoning of MLLMs.
• Score: 82.17582358979884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-training with explicit reasoning traces is common to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, acquiring high-quality reasoning traces is often costly and time-consuming. Hence, the self-improvement paradigm has emerged, enabling MLLMs to self-generate reasoning traces for training without external supervision. Despite its effectiveness, we reveal two shortcomings in the self-improvement training of MLLMs: 1) data imbalance, where simple samples are over-trained, but the challenging yet crucial samples are under-trained; 2) language prior bias, where MLLMs overly rely on linguistic priors while neglecting the visual cues. To this end, we propose VISTA, a vision-aware self-improvement training framework for enhancing the multimodal reasoning of MLLMs. Specifically, VISTA first introduces a prefix resampling strategy to reuse the partial correct reasoning traces for efficient data collection, and then designs a vision-aware attention score to quantify the model's focus on visual information. Extensive experiments show that VISTA can be applied to various post-training scenarios, i.e., supervised fine-tuning and preference learning, and effectively enhances the multimodal reasoning performance across various MLLMs and tasks, e.g., bringing up to +13.66% average performance gains for Qwen2.5-VL-3B-Instruct.

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