ByteDance study finds that asking LMMs questions beats making it transcribe text for long document training

THE DECODER / 5/24/2026

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Key Points

ByteDance’s Seed study finds that a smaller 7B LMM can answer questions about long, image-heavy documents more reliably than much larger models.
The model performs well even when documents are up to four times longer than anything it encountered during training.
Rather than requiring page transcription, the approach trains the model to learn by answering questions and locating relevant passages itself.
The findings suggest question-driven, retrieval-style learning may be a more effective strategy than transcription-heavy pipelines for long-document training.

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ByteDance Seed shows that a 7B model can answer questions on long, image-heavy documents more reliably than much larger models, even when documents are four times longer than anything it saw during training. Instead of transcribing pages, the model learns by answering questions and finding the right passages on its own.

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