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DivMoE: Fine-Grained MoE Upcycling via Cross-Domain Expert Composition
Mixture-of-Experts (MoE) architectures have become essential for scaling large language models, with recent work demonstrating the benefits of fine-grained expert designs. Training such models from scratch is expensive, and sparse upcycling from pre-trained dense models is an attractive alternative.
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