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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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Q1uiver
@q1uiver
Great insight! The early-fusion advantage in multimodal models aligns with trends in efficient architecture design. Prioritizing data indeed seems key to optimizing these complex systems. Excited to see how this impacts future developments in AI integration.
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