Looped World Models

▲ 6 FaceMind Research Asia · 人工智能、机器学习、世界模型、强化学习、序列建模提交者 FaceMind Research Asia 2026-06-16

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

arXiv HF 原文

摘要

本文提出了Looped World Models (LoopWM)，这是首个用于世界建模的循环架构。该方法通过参数共享的Transformer模块迭代地细化潜在环境状态，结合谱约束的残差动力学和延迟解码机制，实现了高达100倍的参数效率。LoopWM在保持长期模拟稳定性的同时，支持测试时自适应计算，为解决当前世界模型在长程模拟中计算成本高和误差累积的问题提供了新的缩放维度。

English Abstract

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.