LoopFold: Parallelizing Recurrent Depth in Looped Transformers
Predicts inputs to later transformer loops so several depths can run in parallel, then corrects their outputs as earlier results arrive. Trains the predictor, corrector and shared backbone together with next-token prediction and measures the accuracy-latency trade-off.
First public: · Added to this list: November 2, 2026
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Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
Alternative generation methods — Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. Some produce written chains of thought.