LaLa-World：用语言条件训练潜在世界模型，规划速度提升13.9倍
LaLa-World: Scaling Latent World Models with Natural Language Actions
这篇论文把世界模型和语言模型接上了：用语言描述动作，一个预测器跨机器人、视频多个领域通用，规划比视频生成基线快13.9倍，做机器人方向值得细看。
论文提出 LaLa-World，一种语言条件潜在世界模型（LWM），用语言同时描述数值动作和视觉变化，使单一预测器能跨仿真、机器人轨迹和图像视频数据联合训练。其平均 MPC 规划成绩超过按领域单独训练的现有 LWM。与基于 RAEv2 的视频生成模型基线对比，LaLa-World 用 6 倍更少的训练成本达到相当规划性能，规划速度快 13.9 倍。其预训练版本可初始化 LaLa-Policy，在 LIBERO 和 RoboCasa 上与 Cosmos Policy 表现相当，推理最多快 5.5 倍。
LaLa-World: Scaling Latent World Models with Natural Language Actions
This paper shows that JEPA-style latent world models (LWMs) can learn across diverse domains and data sources by adopting the language-conditioned interface of video generation models. We establish this approach as an effective and efficient basis for planning with imagination and downstream policy fine-tuning. To this end, we introduce LaLa-World, a Language-conditioned Latent World model that learns from simulation and robot trajectories together with broader image and video data. Two designs make this work. First, we describe both numerical actions and visual changes in language, enabling joint training of a single future predictor across heterogeneous datasets. Second, we build the predictor on a language model, allowing it to interpret language conditions while predicting future latent states. The resulting predictor achieves higher average MPC planning performance than existing LWMs trained separately for each domain or task. Adding image and video data further improves average planning performance and robustness to action phrasing. In a matched comparison with an RAEv2-based video generation model baseline, LaLa-World reaches comparable planning performance with $6\times$ less training cost and $13.9\times$ faster planning. Beyond planning, the pretrained LaLa-World serves as an initialization for LaLa-Policy, which jointly predicts actions and future visual representations. LaLa-Policy performs competitively with Cosmos Policy on LIBERO and RoboCasa, with up to $5.5\times$ faster inference.