# Looped World Models：循环架构实现世界模型参数效率提升达100倍

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-16 08:00
- AIHOT 分数：43
- AIHOT 链接：https://aihot.virxact.com/items/cmqhgiko20476sle10erlqjy6
- 原文链接：https://arxiv.org/abs/2606.18208

## AI 摘要

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模块迭代精炼潜在环境状态，LoopWM在自适应计算中自动匹配每个预测步骤的复杂度，相比传统方法参数效率提升达100倍。该项工作正交于模型规模与训练数据扩展，将迭代潜在深度确立为世界模拟的新扩展轴。

## 正文

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.