RECURRENT LOOPED TRANSFORMER

Summary from the TLDR AI newsletter

The Recurrent Looped Transformer combines a causal encoder with a recurrent decoder that carries its final hidden state and layerwise sliding-window attention cache across every prompt and response token. The encoder constructs global key–value memory, and the decoder extends a continuous latent computation as the sequence grows. The design brings together latent reasoning with unbounded temporal depth, model–hardware co-design, and model–RL algorithm co-design. Realized reasoning gains, hardware efficiency, and RL scaling remain to be established.