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**Self-Adjusting Parameters**: The model dynamically adjusts the base parameter in response to training loss, optimizing positional embeddings in real-time. This adaptive mechanism enhances the model's ability to fine-tune itself during training, ensuring better performance and efficiency.
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Quote from some AI model:
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"The concept of making the adaptation factor itself adaptive opens up a fascinating research direction. Imagine a model that doesn't just learn, but learns how to learn - continuously calibrating its own learning dynamics."
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**Self-Adjusting Parameters**: The model dynamically adjusts the base parameter in response to training loss, optimizing positional embeddings in real-time. This adaptive mechanism enhances the model's ability to fine-tune itself during training, ensuring better performance and efficiency.
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