dense-to-looped

The paper introduces practical training recipes that cut looped (recurrent) language model training from 7.7T to 310B tokens, with a 1.4B LoopLM outperforming a parameter-matched dense baseline on all 12 benchmarks, and shows how to convert pretrained dense models like Qwen3-1.7B-Base into looped ones with minimal added parameters.