Recurrent Looped Transformer: viral technical report claims 'infinite temporal depth' for latent reasoning
Princeton University
Princeton researcher Yifan Zhang's Recurrent Looped Transformer (RLT) carries the decoder's final hidden state and layerwise sliding-window attention cache across every prompt and response token with no reset at the prompt-response boundary, so latent computation depth grows with sequence length (48 encoder + 48 decoder layers in the reference config). It also proposes exact current-policy RL replay that rebuilds parameter-dependent caches under current weights. The report went viral on September 12-13 (111 upvotes, ~16k views on alphaXiv), but the author's own README concedes that reasoning improvements, speedups, and RL scaling are 'research goals rather than measured results', with no code or weights released.
Why it matters
If the recurrence-across-tokens design holds up under training, it offers a route to unbounded latent reasoning depth without deeper physical stacks — but the gap between the 'infinite reasoning depth' framing and the absence of any measured results is itself the story to watch.
Importance: 3/5
Viral technical report (111 upvotes on alphaXiv) extending the recurrent-depth transformer line; claims unverified