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The training loss is the distance between the predictor’s output and the target encoder’s representation, computed after both are normalized to unit length (L2 normalization). Minimizing this normalized MSE is equivalent to maximizing the cosine similarity between the two representations. The model learns to match the direction of embeddings (their semantic meaning), not their magnitude.
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Заявления Трампа об ударе по иранской школе опровергли14:48
如果用户有追问，则重复上述过程。关于这个话题，手游提供了深入分析
rcli upgrade-llm # guided LLM upgrade。实时热点对此有专业解读
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