Yifan Zhang's Recurrent Looped Transformer (RLT) technical report proposes a causal encoder paired with a recurrent decoder that carries its final hidden state and layerwise sliding-window attention cache across every prompt and response token, with no reset at the serving boundary. The reference tied configuration uses 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t decoder blocks after t tokens. The design also specifies hardware-aware execution around the recurrent core and an exact current-policy RL replay contract. No code, weights, or measured results are released yet. The post A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth appeared first on MarkTechPost .
Strategic AI Brief
Impact Analysis
Yifan Zhang's Recurrent Looped Transformer (RLT) technical report proposes a causal encoder paired with a recurrent decoder that carries its final hidden state and layerwise sliding-window attention cache across every prompt and response token, with no reset at the serving boundary.
Market Signal
The reference tied configuration uses 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t decoder blocks after t tokens.
Tactical Warning
The design also specifies hardware-aware execution around the recurrent core and an exact current-policy RL replay contract. No code, weights, or measured results are released yet. The post A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth appeared first on MarkTechPost .