Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts
Studies how to train mixture-of-experts transformers with more than two recurrent passes. Combines stabilized residual updates, reintroduced input information, and loop-specific routing to support deeper reuse of the shared model.
First public: · Added to this list: October 18, 2026
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arXiv:2610.01153 · Initial submission: 2026-10-01
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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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