这篇论文用循环架构解决了世界模型长程预测的计算瓶颈，参数省了100倍还能自适应深度，做仿真和规划的研究者值得看。
Looped World Models（LoopWM）首次将循环架构引入世界建模，通过参数共享的transformer块迭代细化潜在环境状态。相比传统方法，LoopWM在参数效率上提升多达100倍，并能根据预测复杂度自动调整计算深度。该方法将迭代潜在深度确立为世界仿真的新扩展轴，独立于模型规模和训练数据规模。
Looped World Models
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.