From Growing to Looping: A Unified View of Iterative Computation in LLMs
Compares depth growing and weight-tied loops using matched model budgets and interventions on intermediate layers. Finds related patterns of repeated computation and improves reasoning by adding middle-block loops to depth-grown models during inference or continued training.
First public: · Added to this list: November 3, 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.