CopyLoop: Fast Looped Transformer Decoding via Context Copying and Shallow Screening
Speeds up looped language models by copying candidate token spans from the context, screening them at a shallow depth, and verifying them through the remaining loops. Overlaps verification with new proposals and evaluates the approach on code and mathematical reasoning.
First public: · Added to this list: November 2, 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.