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How Far Are We from Removing the Visual Encoder? Scaling Laws for Encoder-Free Multimodal Pretraining

The study compares scaling laws for encoder-free and encoder-based multimodal large language models (MLLMs), revealing that removing the visual encoder shifts compute allocation toward larger models and that encoder-free models may catch up in performance at high compute levels. It also shows that language models adapt to take over visual processing roles as training compute increases.

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PublishedSeptember 28, 2026Lin Chen, Bolin Ni, Qi Yang
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This research provides insights into the scalability of encoder-free multimodal models, suggesting they could become more efficient as compute resources grow, potentially reducing reliance on pretrained visual encoders.

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