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