GuidedWAM：无需重训即可给自动驾驶世界动作模型注入新目标
GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving
给自动驾驶世界模型加新目标不用再重训了，采样时注入能量引导就行，三个公开 WAM 都涨了 1.8 到 3.6 EPDMS。
GuidedWAM 是一个模型无关的能量引导框架，可在采样阶段向冻结的 World Action Model（WAM）注入新的驾驶目标，不需要重新训练。该框架把驾驶目标构造成作用于采样器中间干净动作估计的可微能量，只需对冻结模型的前向访问，并通过引导调度、逐能量归一化和速度有界修正来保持稳定。在 NAVSIM v2 hard-split 上，对三个公开发布的冻结 WAM 平均提升 1.8 到 3.6 EPDMS，每个采样步骤的额外耗时低于 10%。
GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving
World action models (WAMs) for autonomous driving couple the driving policy and the visual world model in a single generative network whose objectives are fixed post-training. Conditioning on a new driving objective requires retraining. We study energy guidance as a general mechanism and present GuidedWAM, a model-agnostic framework for injecting driving objectives into frozen WAMs at sampling time. Our framework constructs each objective as differentiable energies on the sampler's intermediate clean-action estimate, which needs only forward access to the frozen model, and is stabilized by sampling-time guidance scheduling, per-energy normalization, and velocity-bounded correction. On the NAVSIM v2 hard-split, guidance improves all three frozen, publicly released WAMs by 1.8 to 3.6 EPDMS, averaged over three sampler seeds, and adds less than 10% to the sampling time per step. These results position energy guidance as a practical post-training control channel for WAMs.