A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth
In most decoder-only LLMs, nothing computed at the last layer of token t feeds the first layer of token t+1; positions communicate only through attention over cached keys and values. A Princeton researcher’s (Yifan Zhang) technical report, Recurrent Looped Transformer (RLT), proposes closing that loop. The decoder’s final hidden state and its layerwise sliding-window attention (SWA) cache are carried into the next token, across both prompt and response, with no reset at the boundary
A Princeton researcher proposes Recurrent Looped Transformer (RLT), a modification to standard decoder-only language models that carries the decoder's complete state, including its final output and attention caches, forward to every new token without resetting at boundaries. In typical large language models, information from one token reaches the next only through cached attention weights, but RLT closes this loop by feeding the decoder's final hidden state directly into the processing of the next token. The proposed architecture processes 96 logical blocks per token through 48 tied encoder and 48 decoder layers, creating a state path that grows structurally deeper with sequence length while keeping per-token computation fixed. The research presents the design specification, architecture details, and RL training approach, but explicitly reports no measured results on efficiency, reasoning quality, or scaling performance.
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