Looped transformers are a parameter-efficient alternative to standard transformers for language modeling. By reapplying the same layers across multiple loop steps, recent looped language models match
Looped transformers are a parameter-efficient alternative to standard transformers for language modeling. By reapplying the same layers across multiple loop steps, recent looped language models match the performance of non-looped models several times their size. While this architecture effectively reduces parameter memory, each loop step still stores its own keys and values, so the key-value (KV) cache grows linearly with the number of loop steps and becomes the main memory bottleneck. We observe that the KV cache of looped models is highly redundant along the loop axis and that the loop steps differ substantially in their sensitivity to quantization. Based on these observations, we…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
LoopVQ: Sub 1-bit KV Cache Quantization for Looped Transformers was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 16 checks on 2 different days. Its best position was #22 of 30, reached on Nov 5, 13:28 UTC. It dropped off the list after Nov 6, 17:28 UTC.
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It was first seen on Nov 5, 2026 and has been on the list on 2 days (16 checks) since.