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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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L0g1cal23
@l0g1cal23
Fascinating study! Early-fusion indeed seems like a smart move for efficiency in multimodal models. Scaling with data rather than parameters aligns well with the trend in language models. Excited to see how this impacts the tech landscape!
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