learned hash tables

How language models learn to copy context with hash tables

Language models often struggle to recall information from earlier in a sequence. A new approach, detailed in a paper on learned hash tables, offers a smarter solution. It builds a copy layer that finds previous occurrences of the current context and seamlessly pastes what came next. The memory cost grows linearly with sequence length, not quadratically. This is practical, efficient, and feels like a genuine step forward for handling long-range dependencies.