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.

本研究针对世界模型在维持内容稳定性方面的不足，提出了“世界稳定性”的概念，旨在提高生成环境的表现。通过建立评估框架，量化模型在执行一系列动作及其逆向操作后回归初始视角的能力，结果显示当前主流生成模型在稳定性方面面临重大挑战，并探讨了多种改进策略，以推动未来研究的发展。

朝向稳定的世界模型：测量和应对生成环境中的世界不稳定性