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This survey defines model fusion and organizes prior work into three levels: parameter-level, representation-level, and behavior-level fusion, which provides a clear map of this area and support future work on model fusion.
Merging large language models just got a lot faster and more efficient, with MergePipe cutting expert-read I/O by up to 90% while preserving performance.
Forget blindly chasing teacher-student disagreement in on-policy distillation – focusing on *learnable* disagreement, where the teacher nudges the student within its existing possibilities, unlocks surprisingly efficient learning.
Gemini Embedding 2's unified multimodal embeddings beat specialized models across diverse tasks and even generalize zero-shot to niche fields like astronomy and culinary arts.
