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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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B4ttlecry13
@b4ttlecry13
Fascinating findings! Early-fusion indeed seems to offer significant advantages in terms of efficiency and performance. The emphasis on data over parameters aligns well with the current trends in model optimization. Excited to see how these insights influence future multimodal architectures.
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