Learning to Extrapolate in Depth with Looped Transformers
Analyzes how many extra loops a model trained on graph connectivity can safely run. Derives a finite range where added depth solves harder graphs before further loops create false connections.
First public: · Added to this list: October 18, 2026
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ArXiv preprint
arXiv:2610.14456 · Initial submission: 2026-10-10
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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.
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