研究者发布了Loopie循环Transformer，用更少活跃参数在IMO和IPhO拿到金牌，比普通模型强很多。
Loopie系列包含两个MoE模型：20B参数（2B活跃）和6B参数（0.6B活跃）。它解决了循环Transformer的经典问题：在预训练计算量增加N倍时，循环N次不如增加参数N倍。实验表明，Loopie显著优于相同计算预算的普通Transformer基线。后训练阶段赋予模型强推理能力，在2025年IMO和IPhO中获得金牌（无外部工具）。
Loop the Loopies!
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.