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Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging

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Training-free model merging to bring reasoning to vision-language models

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Open Source
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About Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging

This paper, 'Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging,' presents a training-free method to transfer reasoning capabilities from Large Language Models (LLMs) to Vision-Language Models (VLMs) via model merging. The authors merge models across modalities—combining parameters of different models—to compose perception and reasoning. Through extensive experiments, they show that model merging successfully incorporates LLM reasoning into VLMs without additional training. The study also investigates internal mechanisms, finding that perception abilities are primarily encoded in early layers, while reasoning relies on middle-to-late layers. After merging, all layers contribute to reasoning while perception distribution remains largely unchanged. Accepted at ICML 2025, with code publicly available.

Key Features

Training-free model merging across modalities (LLM + VLM)
Transfers reasoning capabilities from LLMs to VLMs
Provides insights into layer-wise contributions: perception in early layers, reasoning in middle-to-late layers
Demonstrates that after merging, all layers contribute to reasoning while perception distribution unchanged
Accepted at ICML 2025 (camera-ready version)
Publicly available code

Pros & Cons

Pros
  • Training-free: no additional fine-tuning or data required
  • Provides interpretable insights into how perception and reasoning are distributed across model layers
  • Demonstrates successful cross-modal ability transfer
  • Open-source code available for reproducibility
  • Rigorous experimental validation accepted at top conference (ICML)
Cons
  • Research-stage method; not packaged as a ready-to-use tool
  • Requires access to both a vision-language model and a large language model parameters
  • Effectiveness may depend on model architectures and initial capabilities
  • Does not address multimodal hallucination or other VLM weaknesses beyond reasoning transfer
  • Potentially limited to specific model families used in experiments

Best For

Enhancing reasoning abilities of vision-language models without retrainingStudying internal mechanisms of perception and reasoning in multimodal modelsComposing capabilities from separate models for multimodal integrationExploring training-free methods for model improvement and transfer learning

FAQ

What is the main contribution of this paper?
The paper proposes a training-free method to transfer reasoning abilities from LLMs to VLMs via model merging, and provides insights into how perception and reasoning are distributed across model layers.
Does the method require additional training?
No, the model merging approach is training-free—it simply combines parameters of different models.
What models are used in the experiments?
The paper experiments with merging VLMs and LLMs, but specific model names are not listed in the abstract. The full paper likely details the architectures.
Where can I find the code?
The code is publicly available at a URL linked in the paper's abstract.
Has this work been peer-reviewed?
Yes, the paper has been accepted at ICML 2025 and a camera-ready version is available.