title: Perceptual Decoupling for Scalable Multi-modal Reasoning via
    Reward-Optimized Captioning
summary: 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.
paper_authors._id: 68428cc5dca9acf2341bb86e
paper_authors.name: Yunhao Gou
paper_authors._id: 68428cc5dca9acf2341bb86f
paper_authors.name: Kai Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:48:47.392Z
paper_authors._id: 65bf3b304b5f8c270d50d6c8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65bf3b304b5f8c270d50d6c8/iSC_Em-JtfcsKFoGw4KFc.jpeg
paper_authors.fullname: Kai Chen
paper_authors.type: user
paper_authors.user: KaiChen1998
paper_authors._id: 68428cc5dca9acf2341bb870
paper_authors.name: Zhili Liu
paper_authors._id: 68428cc5dca9acf2341bb871
paper_authors.name: Lanqing Hong
paper_authors._id: 68428cc5dca9acf2341bb872
paper_authors.name: Xin Jin
paper_authors._id: 68428cc5dca9acf2341bb873
paper_authors.name: Zhenguo Li
paper_authors._id: 68428cc5dca9acf2341bb874
paper_authors.name: James T. Kwok
paper_authors._id: 68428cc5dca9acf2341bb875
paper_authors.name: Yu Zhang
paper_title: Perceptual Decoupling for Scalable Multi-modal Reasoning via
    Reward-Optimized Captioning
paper_summary: 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.
paper_ai_summary: A reasoning-aligned reinforcement learning strategy enhances visual representations in multi-modal large language models by optimizing captions for downstream reasoning tasks.
paper_ai_keywords: slow-thinking language models multi-modal large language models reasoning capabilities visual extractor captions reinforcement learning visual grounding