The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer

2025-04-14

AI Summary

This work addresses architectural redundancy and inefficient cross-modal alignment in multimodal large language models (MLLMs). We propose SAIL, the first end-to-end unified vision-language large model based on a single Transformer backbone. SAIL eliminates the pre-trained ViT encoder and directly processes raw pixels and text through a unified architecture featuring hybrid attention mechanisms and multimodal positional encoding for pixel-level joint modeling. Key contributions include: (1) the first encoder-free monolithic MLLM architecture; (2) empirical demonstration that architectural simplification enhances scalability and enables novel cross-modal information flow; and (3) visual representation capability matching that of ViT-22B. On multi-task benchmarks, SAIL matches or exceeds modular MLLMs; it significantly outperforms lightweight counterparts on vision segmentation tasks, while simultaneously improving training/inference efficiency and cross-modal alignment quality.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

This paper introduces SAIL, a single transformer unified multimodal large language model (MLLM) that integrates raw pixel encoding and language decoding within a singular architecture. Unlike existing modular MLLMs, which rely on a pre-trained vision transformer (ViT), SAIL eliminates the need for a separate vision encoder, presenting a more minimalist architecture design. Instead of introducing novel architectural components, SAIL adapts mix-attention mechanisms and multimodal positional encodings to better align with the distinct characteristics of visual and textual modalities. We systematically compare SAIL's properties-including scalability, cross-modal information flow patterns, and visual representation capabilities-with those of modular MLLMs. By scaling both training data and model size, SAIL achieves performance comparable to modular MLLMs. Notably, the removal of pretrained ViT components enhances SAIL's scalability and results in significantly different cross-modal information flow patterns. Moreover, SAIL demonstrates strong visual representation capabilities, achieving results on par with ViT-22B in vision tasks such as semantic segmentation. Code and models are available at https://github.com/bytedance/SAIL.

Problem

• Unified multimodal model integrating vision and language
• Eliminates need for separate vision encoder
• Enhances scalability and cross-modal information flow

Innovation

• Single transformer unified multimodal model
• Mix-attention mechanisms for modality alignment
• Eliminates separate vision encoder