Looped Language Models: A Survey and Meta-Analysis of Recurrent-Depth Transformers
Surveys models that reuse transformer layers across repeated hidden-state updates. Compares their architecture, training and inference choices, separating parameter-matched results from compute-matched results.
First public: · Added to this list: October 22, 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.
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.