The Scalability of Simplicity: Empirical Analysis of Vision-Language
  Learning with a Single Transformer
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.
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Weixian Lei
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Jiacong Wang
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Haochen Wang
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HaochenWang
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HaochenWang
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Xiangtai Li
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Xiangtai Li
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LXT
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Jun Hao Liew
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Jiashi Feng
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Zilong Huang
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2025-05-14T07:36:51.308Z
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Zilong Huang
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The Scalability of Simplicity: Empirical Analysis of Vision-Language
  Learning with a Single Transformer
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.
SAIL, a unified multimodal large language model, integrates raw pixel encoding and language decoding using mix-attention mechanisms and achieves performance comparable to modular MLLMs without a separate vision encoder.
single transformer unified multimodal large language model raw pixel encoding language decoding mix-attention mechanisms multimodal positional encodings pre-trained vision transformer scalability cross-modal information flow patterns visual representation capabilities semantic segmentation