How to Loop Your Transformer: Loop Scaling, Specialization, and Parallel Inference
Compares which transformer blocks to repeat, how to give each repetition its own small adapter, and how to run loops in parallel during decoding. Separates the effects of state handoffs and shared attention caches across model sizes and compute budgets.
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.