Deploying safety-critical agents requires anticipating the consequences of actions before they are executed. While world models offer a paradigm for this proactive foresight, current approaches relying on visual simulation incur prohibitive latencies, often exceeding several seconds per step. In this work, we challenge the assumption that visual processing is necessary for failure prevention. We show that a trained policy's latent state, combined with its planned actions, already encodes sufficient information to anticipate action outcomes, making visual simulation redundant for failure prevention. To this end, we introduce DILLO (DIstiLLed Language-ActiOn World Model), a fast steering layer that shifts the paradigm from "simulate-then-act" to "describe-then-act." DILLO is trained via cross-modal distillation, where a privileged Vision Language Model teacher annotates offline trajectories and a latent-conditioned Large Language Model student learns to predict semantic outcomes. This creates a text-only inference path, bypassing heavy visual generation entirely, achieving a 14x speedup over baselines. Experiments on MetaWorld and LIBERO demonstrate that DILLO produces high-fidelity descriptions of the next state and is able to steer the policy, improving episode success rate by up to 15 pp and 9.3 pp on average across tasks.

翻译：部署安全关键型代理需要在执行动作前预判其后果。尽管世界模型为这种主动预判提供了范式，但当前依赖视觉模拟的方法会带来难以承受的延迟（通常每步超过数秒）。本研究挑战了"预防失败必须依赖视觉处理"这一假设，证明已训练策略的隐状态结合其规划动作已编码了足够信息来预测动作结果，因此视觉模拟对于失败预防是冗余的。为此，我们提出DILLO（蒸馏语言-动作世界模型），这是一种快速引导层，将范式从"模拟-然后-行动"转向"描述-然后-行动"。DILLO通过跨模态蒸馏训练：特权视觉语言模型教师标注离线轨迹，隐状态条件化的大语言模型学生则学习预测语义结果。这创建了纯文本推理路径，完全绕过繁重的视觉生成，相比基线实现了14倍加速。在MetaWorld和LIBERO上的实验表明，DILLO能生成下一状态的高保真描述，并有效引导策略，使任务平均成功率和各任务成功率分别提升高达15个和9.3个百分点。