Gated Linear Attention combines gates and linear attention
Published on September 20, 2026, the item describes a transformer formulation with linear attention, data-dependent gates, and an equivalent representation as an RNN with matrix-valued hidden states.
The article presents Gated Linear Attention, a transformer formulation combining linear attention with data-dependent gates. According to the summary, it can also be expressed as an RNN with matrix-valued hidden states. The text also describes a hardware-efficient training algorithm.
The approach is presented as an alternative to softmax attention, with linear-time inference and hardware-efficient training. The summary provides no quantitative results or implementation details. To assess these claims, consult the original article and check its formulation, methods, and experimental evidence; do not infer specific performance from this summary alone.