Revisiting InternVL: A Systematic Technical Framework for Building Powerful Open-Source Vision-Language Models

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Developing powerful vision-language models (VLMs) requires a comprehensive system design.
• The InternVL series (v1.0-v3.0) represents a significant advancement in VLM research.
• Existing approaches often lack a systematic framework for scaling and performance optimization.

Purpose of the Study:

• To present a systematic framework for constructing large-scale, high-performance VLMs based on the InternVL series evolution.
• To detail three pivotal technical shifts: Perceptual Scaling, Multimodal Alignment Scaling, and Native Multimodal Pre-training.
• To offer a reproducible roadmap for future multimodal research.

Main Methods:

• Developed a 6-billion parameter vision encoder (InternViT-6B) and a VLM-oriented alignment strategy for fine-grained perception.
• Implemented a multimodal dynamic high-resolution (mDHR) mechanism for unified input handling (single-image, multi-image, video).
• Transitioned to a native multimodal continual pre-training paradigm, jointly optimizing interleaved multimodal and text-only data.

Main Results:

• Models built on the framework achieve state-of-the-art performance among open-source VLMs.
• Performance rivals leading proprietary vision-language systems across various benchmarks.
• Demonstrated deep synergy between visual-world knowledge internalization and preserved linguistic proficiency.

Conclusions:

• The presented framework provides a systematic and reproducible approach to building high-performance VLMs.
• The technical shifts in perceptual and multimodal alignment scaling are crucial for advancing VLM capabilities.
• Native multimodal pre-training enhances model synergy and performance, offering a path for future research.