long-context modeling
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AI Model Learns While Reading
Read Full Article: AI Model Learns While Reading
A collaborative effort by researchers from Stanford, NVIDIA, and UC Berkeley has led to the development of TTT-E2E, a model that addresses long-context modeling as a continual learning challenge. Unlike traditional approaches that store every token, TTT-E2E continuously trains while reading, efficiently compressing context into its weights. This innovation allows the model to achieve full-attention performance at 128K tokens while maintaining a constant inference cost. Understanding and improving how AI models process extensive contexts can significantly enhance their efficiency and applicability in real-world scenarios.
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