title: The Scalability of Simplicity: Empirical Analysis of Vision-Language
    Learning with a Single Transformer
summary: 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.
paper_authors._id: 67ff2aa6a0346c2e622afdb2
paper_authors.name: Weixian Lei
paper_authors._id: 67ff2aa6a0346c2e622afdb3
paper_authors.name: Jiacong Wang
paper_authors._id: 67ff2aa6a0346c2e622afdb4
paper_authors.name: Haochen Wang
paper_authors._id: 67ff2aa6a0346c2e622afdb5
paper_authors.name: Xiangtai Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T08:42:51.715Z
paper_authors._id: 63958b4414513eaf9029ebf1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
paper_authors.fullname: Xiangtai Li
paper_authors.type: user
paper_authors.user: LXT
paper_authors._id: 67ff2aa6a0346c2e622afdb6
paper_authors.name: Jun Hao Liew
paper_authors._id: 67ff2aa6a0346c2e622afdb7
paper_authors.name: Jiashi Feng
paper_authors._id: 67ff2aa6a0346c2e622afdb8
paper_authors.name: Zilong Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-14T07:36:51.308Z
paper_authors._id: 63a430ec84a6a25c65bff20c
paper_authors.avatarUrl: /avatars/3ab51214c491f28d8fe6b70c373d72aa.svg
paper_authors.fullname: Zilong Huang
paper_authors.type: user
paper_authors.user: speedinghzl
paper_title: The Scalability of Simplicity: Empirical Analysis of Vision-Language
    Learning with a Single Transformer
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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