Loop the Loopies!

AK's Picks LLM PTSAD MoE ML TAVR MEM
2026年07月17日
我们推出了Loopie,这是迄今为止最强大的循环式Transformer模型。Loopie系列包含两个混合专家(MoE)模型:一个参数总量为200亿、每次前向传播仅激活20亿参数的模型;另一个参数总量为60亿、每次前向传播仅激活6亿参数的模型。长期以来,循环式Transformer一直面临一项挑战:在预训练计算量提升N倍的前提下,将模型参数量扩大N倍,通常比将同一模型循环执行N次更具优势。Loopie成功克服了这一挑战。大量消融实验(包括与同等计算预算下训练的300亿参数—30亿激活参数基线模型的对比)表明,Loopie显著优于同计算量下训练的各类标准Transformer基线模型。我们提出的全新后训练流程,赋予了Loopie出色的推理能力。在2025年国际数学奥林匹克竞赛(IMO)和国际物理奥林匹克竞赛(IPhO)中,Loopie在不借助任何外部工具的情况下,均达到了金牌水平。
We present Loopie, the most powerful looped Transformer to date. The Loopie series consists of two Mixture-of-Experts (MoE) models: a 20B-parameter model with 2B active parameters and a 6Bparameter model with 0.6B active parameters. Looped Transformers have long faced a challenge: given an N-fold increase in pre-training compute, increasing the parameter count by a factor of N usually outperforms looping a model N times. Loopie addresses this challenge. Extensive ablation studies, including comparisons with a vanilla 30B-A3B model, show that Loopie substantially outperforms vanilla Transformer baselines trained with the same compute budget. Our novel post-training pipeline equips Loopie with strong reasoning abilities. At the 2025 IMO and IPhO, Loopie achieves gold-medal performance without tools.
许愿