循环20亿参数，IMO金牌
AI模型通常越做越大，但一篇新研究证明：把一个小模型循环使用多次，效果能超过同算力下的大模型。研究者训练了20亿参数（实际激活20亿）的循环Transformer“Loopie”，在2025年国际数学和物理奥赛中不借助工具拿到金牌。关键在于新的训练方法让循环不退化，反而增强推理。它不是你明天能用上的，但暗示了未来AI可能更小、更高效。
📄 原文摘要(英文)
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.