The paper introduces Elastic Latent Recurrence (ELR), a training recipe that enables looped language models to match the accuracy of non-recurrent models at a single loop while allowing additional loops to improve accuracy with more compute. ELR achieves this through gated state updates with learned step sizes, mixed loop counts during training, and conditioning on loop index and budget. The method also reduces training and inference overhead in Mixture-of-Experts models and produces a single checkpoint, Koi‑4B, that performs competitively with state‑of‑the‑art models on multiple benchmarks.
By Faisal Ladhak, Shantanu Acharya, Santiago Akle Serano, Simeng Sun, Boris Ginsburg, Krishna C Puvvada
The paper presents practical training recipes for looped language models, showing that a compute‑efficient pipeline can reduce the training budget from 7.7 T tokens to 310 B tokens while maintaining strong reasoning performance. In controlled experiments, a 1.4 B LoopLM outperforms a parameter‑matched dense model on 12 benchmarks, achieving significant gains on GSM8K, MATH, and DROP, and approaches a 3.9 B dense model at matched inference compute. The authors also provide a minimal recipe to convert pretrained dense models into looped ones, demonstrating improvements on Qwen3‑1.7B‑Base across multiple data regimes.
By Andrei Marchenko, Viacheslav Bezrukov, Oleg Kashurin, Inessa Fedorova, Dmitry Bocharov, Yuliana Shakhvalieva, Maria Tikhonova, Valerii Ternovskii
RecurTrace introduces adaptive latent reasoning for language models by allowing each looped layer to attend to its own past states and by using a halting head to decide when to stop iterating. This approach overcomes two limitations of prior latent recurrence methods: limited access to earlier computations and a fixed loop count that mismatches input difficulty. In experiments on MathQA, RecurTrace achieves 56.9% accuracy with an average of 2.0 loops, outperforming fixed‑depth baselines and other adaptive methods, and it also improves generation accuracy across a range of model sizes.
By Yuxiang Wang, Kunyu Feng, Yingda Shen, Haoning Xu, Junyu Wang, Zhizheng Wu
The paper investigates how looping—reusing transformer weights to deepen models without adding parameters—affects performance under different constraints. Experiments on dense decoder‑only transformers up to 1 B parameters show that looping the full transformer yields best quality for a fixed parameter budget, while looping a middle block is preferable when training FLOPs are limited. Adding loop‑specific low‑rank adapters improves quality, but decoding slows linearly with loops; a parallel loop design that passes hidden states across token positions can mitigate this slowdown, though careful handling of token embeddings is needed to retain gains.
By Chanakya Ekbote, Artidoro Pagnoni, Devendra Singh Sachan, Shruti Bhosale, Mike Lewis, Maryam Fazel-Zarandi, Shashank Rajput, Sujan Kumar Gonugondla
arXiv:2610.31943v1 Announce Type: cross
Abstract: Looped language models apply one block of layers repeatedly, and the design pays off only if depth is reusable: loop steps beyond the trained budget...
By Ivan Viakhirev, Kirill Borodin, Maxim Abramov, Grach Mkrtchian
arXiv:2606. 18023v1 Announce Type: cross Abstract: Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count.
By Jian Yang, Shawn Guo, Wei Zhang, Tianyu Zheng, Yaxin Du, Haau-Sing Li, Jiajun Wu, Yue Song, Yan Xing, Qingsong Cai, Zelong Huang, Chuan Hao, Ran Tao, Xianglong Liu, Wayne Xin Zhao, Mingjie Tang, Weifeng Lv, Ming Zhou, Bryan Dai