Loading market data...
ai

A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

MarkTechPost
Read Full Article at MarkTechPost
Share:PostShare
Ad Slot — In-Article (728x90)

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.

This is a summary. For the full story, read the original article at MarkTechPost.

Original source: MarkTechPost

Ad Slot — Below Article (300x250)