自适应潜在容量用于世界模型
Adaptive Latent Capacity for World Models
AI总结：
本文提出自适应潜在容量世界模型ALeWM，通过JEPA架构和MixSIGReg正则化动态分配潜在表示容量，在受控系统和视觉控制任务中实现更高成功率与更低规划成本。
AI中文摘要：
我们引入了自适应LeWorldModel（ALeWM），这是一种基于联合嵌入预测架构（JEPA）的世界模型，它学习将预测信息集中在宽潜在表示中的紧凑前缀上。为了鼓励这种排序，ALeWM学习一个序列条件下的前缀长度分布，并训练预测器从采样的输入前缀估计完整的下一嵌入。由于标准的防坍缩目标鼓励潜在坐标之间的变化，但并未按预测重要性组织它们，我们还引入了MixSIGReg。MixSIGReg通过将掩码嵌入与高斯活动前缀和其余坐标中的零的先前加权混合进行正则化。因此，ALeWM目标鼓励早期坐标保留对预测和递归规划有用的信息。我们的分析表明，MixSIGReg使用的混合分布为较早的坐标块分配更高的方差，为较晚的坐标块分配较低的方差。此外，我们表明，在特定假设下，通过将最有用的预测信息放置在较早的块中，可以最小化预测误差。在实证中，我们在具有已知状态变量的受控动态系统和目标条件视觉控制中研究了ALeWM的行为。我们表明，ALeWM始终比调整后的固定宽度LeWM获得更高的平均成功率，且平均规划容量更低。
英文摘要：
We introduce Adaptive LeWorldModel (ALeWM), a world model based on a joint-embedding predictive architecture (JEPA) that learns to concentrate predictive information in compact prefixes of a wide latent representation. To encourage this ordering, ALeWM learns a sequence-conditioned distribution over prefix lengths and trains the predictor to estimate the full next embedding from a sampled input prefix. As standard anti-collapse objectives encourage variation across latent coordinates and do not organize them by predictive importance, we also introduce MixSIGReg. MixSIGReg regularizes the masked embeddings against a prior-weighted mixture with Gaussian active prefixes and zeros in the remaining coordinates. As a result, the ALeWM objective encourages early coordinates to retain information useful for prediction and recursive planning. Our analysis shows that the mixture distribution used by MixSIGReg assigns higher variance to earlier coordinate blocks and lower variance to later ones. In addition, we show that, under specified assumptions, prediction error is minimized by placing the information most useful for prediction in earlier blocks. Empirically, we study the behavior of ALeWM in a controlled dynamical system with known state variables and in goal-conditioned visual control. We show that ALeWM consistently achieves higher mean success rates than tuned fixed-width LeWM, with lower planning capacity on average.
