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Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

Research Multimodal & Generative

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TL;DR - This paper studies image tokenizers as the “visual language” of unified autoregressive multimodal models using task-specific training losses. It finds that tokenizer quality cannot be judged by reconstruction alone and that image-to-text loss is a comparatively consistent predictor of downstream generation and visual-understanding performance.

  • Text, image, text-to-image, and image-to-text losses scale differently and can rank tokenizers differently.
  • Across tokenizers, text-to-image loss is confounded by differing image-token spaces, while image-to-text loss uses a shared text vocabulary and is more comparable.
  • Better image reconstruction does not necessarily produce lower task losses or stronger downstream results.
  • Tokenizer design—including discriminator use, semantic supervision, and vocabulary size—affects joint image-text learning and can influence text modeling.

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Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

arXiv cs.CV Siting Li, Zhengyang Wang, Simon Shaolei Du, Xi Chen, Yang Liu 2026-09-08 arXiv:2609.09143
Public signals Hugging Face upvotes 28
Providers: Hugging Face · Upvotes 28 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:22:09.030627 UTC

TL;DR - This paper studies image tokenizers as the “visual language” of unified autoregressive multimodal models using task-specific training losses. It finds that tokenizer quality cannot be judged by reconstruction alone and that image-to-text loss is a comparatively consistent predictor of downstream generation and visual-understanding performance.

  • Text, image, text-to-image, and image-to-text losses scale differently and can rank tokenizers differently.
  • Across tokenizers, text-to-image loss is confounded by differing image-token spaces, while image-to-text loss uses a shared text vocabulary and is more comparable.
  • Better image reconstruction does not necessarily produce lower task losses or stronger downstream results.
  • Tokenizer design—including discriminator use, semantic supervision, and vocabulary size—affects joint image-text learning and can influence text modeling.
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