Sparse Delta Memory boosts RNN capacity 1,000x without extra compute

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Researchers have introduced Sparse Delta Memory (SDM), a lightweight architectural modification to gated linear recurrent neural networks that expands effective memory state size by three orders of magnitude. The innovation works by sparsifying the update rule of Gated DeltaNet, replacing dense key-value outer products with a sparse read/write layer. Crucially, SDM achieves this capacity increase while keeping both FLOPs and parameter counts identical to the dense baseline, removing the traditional trade-off between model capacity and computational cost. Under iso-FLOP conditions, models using SDM showed significantly stronger performance on in-context learning and long-context retrieval benchmarks. The findings suggest developers can swap standard gated linear RNNs for SDM to gain up to a 1,000x virtual memory boost at no additional computational expense.

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