Perceptual Decoupling for Scalable Multi-modal Reasoning via
  Reward-Optimized Captioning
Authors: name: Yunhao Gou, name: Kai Chen, name: Zhili Liu, name: Lanqing Hong, name: Xin Jin, name: Zhenguo Li, name: James T. Kwok, name: Yu Zhang

Recent advances in slow-thinking language models (e.g., OpenAI-o1 and
DeepSeek-R1) have demonstrated remarkable abilities in complex reasoning tasks
by emulating human-like reflective cognition. However, extending such
capabilities to multi-modal large language models (MLLMs) remains challenging
due to the high cost of retraining vision-language alignments when upgrading
the underlying reasoner LLMs. A straightforward solution is to decouple
perception from reasoning, i.e., converting visual inputs into language
representations (e.g., captions) that are then passed to a powerful text-only
reasoner. However, this decoupling introduces a critical challenge: the visual
extractor must generate descriptions that are both faithful to the image and
informative enough to support accurate downstream reasoning. To address this,
we propose Reasoning-Aligned Perceptual Decoupling via Caption Reward
Optimization (RACRO) - a reasoning-guided reinforcement learning strategy that
aligns the extractor's captioning behavior with the reasoning objective. By
closing the perception-reasoning loop via reward-based optimization, RACRO
significantly enhances visual grounding and extracts reasoning-optimized
representations. Experiments on multi-modal math and science benchmarks show
that the proposed RACRO method achieves state-of-the-art average performance
while enabling superior scalability and plug-and-play adaptation to more
advanced reasoning LLMs without the necessity for costly multi-modal
re-alignment.