循環世界模型

Looped World Models

June 16, 2026

作者: Hongyuan Adam Lu, Z. L. Victor Wei, Qun Zhang, Jinrui Zeng, Bowen Cao, Lingwei Meng, Mocheng Li, Zezhong Wang, Haonan Yin, Naifu Xue, Minyu Chen, Cenyuan Zhang, Zefan Zhang, Hao Wei, Jiawei Zhou, Haoran Xu, Hao Yang, Ronglai Zuo, Tongda Xu, Yonghao Li, Jian Chen, Hebin Wang, Zeyu Gao, Yang Li, Wei Zhao, Qimin Zhong, Siqi Liu, Yumeng Zhang, Leyan Cui, Zhangyu Wang, Wai Lam

cs.AI

摘要

當前世界模型面臨一個根本性的矛盾：要實現精確的長期模擬需要深度計算，但越深的模型不僅部署成本高昂，也更容易產生累積誤差。我們透過提出循環世界模型（LoopWM）來解決此問題，這是首個用於世界建模的循環架構。本方法透過參數共享的Transformer模組反覆迭代地精煉潛在環境狀態。相較於傳統方法，這能實現高達100倍的參數效率，同時具備自適應計算能力，可自動調節深度以匹配每個預測步驟的複雜度。與擴展模型規模和訓練資料不同，LoopWM將迭代式潛在深度確立為世界模擬的新擴展維度，這可能極大地推動領域發展。

English

Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding errors. We resolve this by introducing Looped World Models (LoopWM), which are the first looped architectures for world modelling. Our method iteratively refines latent environment states through a parameter-shared transformer block. This yield up to 100x parameter efficiency over conventional approaches with adaptive computation that automatically scales depth to match the complexity of each prediction step. Orthogonal to scaling model size and training data, LoopWM establishes iterative latent depth as a new scaling axis for world simulation, which might significantly push the community forward.