提出评估生成环境稳定性的新方法，发现主流模型存在场景失真问题。
Toward Stable World Models: Measuring and Addressing World Instability in Generative Environments
- 用动作-逆动作循环测试环境一致性，量化稳定性
- 主流扩散模型在长时间生成中显著丢失初始场景信息
- 适合关注生成环境可靠性与强化学习应用的研究者
我们提出一种新的研究，旨在提升世界模型保持内容一致的能力，聚焦于我们称之为‘世界稳定性’的属性。基于扩散的生成模型虽在构建沉浸式、逼真的环境中取得进展，广泛应用于强化学习和交互式游戏引擎，但其往往忽视对已生成场景的长期保留——这一缺陷可能引入噪声，影响智能体学习并损害安全关键场景下的性能。本文提出一个评估框架：让世界模型执行一系列动作后执行逆动作，返回初始视角，通过比较起始与结束观测来量化一致性。对当前最先进的扩散型世界模型的全面评估显示，实现高世界稳定性仍面临重大挑战。此外，我们探讨了多种改进策略以增强稳定性。结果凸显了世界稳定性在世界建模中的重要性，并为该领域未来研究提供了可操作的洞见。
原文摘要 · Abstract (English)
We present a novel study on enhancing the capability of preserving the content in world models, focusing on a property we term World Stability. Recent diffusion-based generative models have advanced the synthesis of immersive and realistic environments that are pivotal for applications such as reinforcement learning and interactive game engines. However, while these models excel in quality and diversity, they often neglect the preservation of previously generated scenes over time--a shortfall that can introduce noise into agent learning and compromise performance in safety-critical settings. In this work, we introduce an evaluation framework that measures world stability by having world models perform a sequence of actions followed by their inverses to return to their initial viewpoint, thereby quantifying the consistency between the starting and ending observations. Our comprehensive assessment of state-of-the-art diffusion-based world models reveals significant challenges in achieving high world stability. Moreover, we investigate several improvement strategies to enhance world stability. Our results underscore the importance of world stability in world modeling and provide actionable insights for future research in this domain.
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