The Surprising Effectiveness of Shared Memory in Looped Transformers
Shares a long-context KV memory written by the first loop across later loops, which also keep a short local memory. Studies this architecture during pretraining and examines how shared memory changes representations and gradient flow.
First public: · Added to this list: October 18, 2026
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arXiv:2610.02383 · 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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