From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models

• URL: http://arxiv.org/abs/2605.20177v1
• Date: Tue, 19 May 2026 17:58:40 GMT
• Title: From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
• Abstract summary: We study the interplay between perception and reasoning in vision-language models (VLMs) post-training by decomposing their capabilities into three separate training stages.<n>We show that models trained with our approach achieve 1.5% higher reasoning accuracy with 20.8% shorter reasoning traces.
• Score: 66.95781712577315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in vision-language models (VLMs) emphasize long chain-of-thought reasoning; yet, we find that their performance on visual tasks is primarily limited by a lack of visual perception as opposed to reasoning itself. In this work, we systematically study the interplay between perception and reasoning in VLM post-training by decomposing their capabilities into three separate training stages: visual perception, visual reasoning, and textual reasoning, incorporating specialized training data. We demonstrate that visual perception (a) requires targeted optimization with specialized data; (b) serves as a fundamental scaffold that should be solidified through staged training before refining visual reasoning; and (c) is more effectively learned via RL than caption-based SFT. Our experiments across multiple VLMs demonstrate that staged training consistently improves both visual perception and reasoning performance over merged training. Notably, models trained with our approach achieve 1.5% higher reasoning accuracy with 20.8% shorter reasoning traces, suggesting that superior perception reduces the need for excessive reasoning. Furthermore, we show that this capability-based staging represents a new curriculum dimension orthogonal to traditional difficulty-based curricula, and combining both yields further additive gains. Our staged-training models achieve superior performance among open-weight VLMs, establishing advanced results on several visual math and perception (e.g., +5.2% on WeMath and +3.7% on RealWorldQA) tasks compared with the base counterpart.

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