循环世界模型

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

当前世界模型面临一个根本性矛盾：可靠的长程仿真需要深层计算，但更深层的模型部署成本高且易产生累积误差。我们通过引入循环世界模型（LoopWM）解决此问题，这是首个采用循环架构进行世界建模的方法。该方法通过参数共享的Transformer模块，迭代式地精炼潜在环境状态。与传统方法相比，这实现了高达100倍的参数效率，同时具备自适应计算能力，可自动调整深度以匹配每个预测步骤的复杂度。正交于模型规模与训练数据的扩展，LoopWM将迭代潜在深度确立为世界仿真的新扩展维度，这或将显著推动领域发展。

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.