Emerging Properties in Unified Multimodal Pretraining
Unifying multimodal understanding and generation has shown impressive
capabilities in cutting-edge proprietary systems. In this work, we introduce
BAGEL, an open0source foundational model that natively supports multimodal
understanding and generation. BAGEL is a unified, decoder0only model pretrained
on trillions of tokens curated from large0scale interleaved text, image, video,
and web data. When scaled with such diverse multimodal interleaved data, BAGEL
exhibits emerging capabilities in complex multimodal reasoning. As a result, it
significantly outperforms open-source unified models in both multimodal
generation and understanding across standard benchmarks, while exhibiting
advanced multimodal reasoning abilities such as free-form image manipulation,
future frame prediction, 3D manipulation, and world navigation. In the hope of
facilitating further opportunities for multimodal research, we share the key
findings, pretraining details, data creation protocal, and release our code and
checkpoints to the community. The project page is at https://bagel-ai.org/
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Chaorui Deng
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Deyao Zhu
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Kunchang Li
claimed_verified
2025-05-21T08:41:06.469Z
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Kunchang Li
user
Andy1621
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Chenhui Gou
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Chenhui Gou
user
gouc
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Feng Li
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Zeyu Wang
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Shu Zhong
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Weihao Yu
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Weihao Yu
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Xiaonan Nie
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Xiaonan Nie
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codecaution
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Ziang Song
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2025-05-21T10:06:07.780Z
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Song
user
Ziang
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Guang Shi
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Haoqi Fan
Emerging Properties in Unified Multimodal Pretraining
Unifying multimodal understanding and generation has shown impressive
capabilities in cutting-edge proprietary systems. In this work, we introduce
BAGEL, an open0source foundational model that natively supports multimodal
understanding and generation. BAGEL is a unified, decoder0only model pretrained
on trillions of tokens curated from large0scale interleaved text, image, video,
and web data. When scaled with such diverse multimodal interleaved data, BAGEL
exhibits emerging capabilities in complex multimodal reasoning. As a result, it
significantly outperforms open-source unified models in both multimodal
generation and understanding across standard benchmarks, while exhibiting
advanced multimodal reasoning abilities such as free-form image manipulation,
future frame prediction, 3D manipulation, and world navigation. In the hope of
facilitating further opportunities for multimodal research, we share the key
findings, pretraining details, data creation protocal, and release our code and
checkpoints to the community. The project page is at https://bagel-ai.org/
BAGEL, an open-source foundational model trained on diverse multimodal data, significantly outperforms existing models in both generation and understanding tasks.
multimodal understanding multimodal generation foundational model decoder-only model trillions of tokens large-scale interleaved data complex multimodal reasoning free-form image manipulation future frame prediction 3D manipulation world navigation