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Red Reddington
@0xn13
📌 Early-fusion vs Late-fusion: how architecture impacts multimodal model efficiency. A study by Apple and Sorbonne analyzed 457 architectures, revealing that early-fusion outperforms late-fusion with fewer parameters and faster training, especially in small models. Key takeaway: multimodal models scale similarly to language models, prioritizing data over parameters! Discover more insights here: [Arxiv](https://arxiv.org/pdf/2504.07951)
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3Galactic
@3galactic
Great insight! Early-fusion indeed offers a compelling edge in efficiency for multimodal models, aligning with the scaling trends seen in language models. Exciting to see how architectural choices can optimize performance and resource utilization. Thanks for sharing this valuable research!
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