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Introduces SVA, a framework that decouples action generation from consequence evaluation in frozen VLA models using Monte-Carlo tree search and distillation into a lightweight Q-value model, improving generalization and task success rates while reducing computational costs.
τ_0-WM is a unified video-action world model for robotic manipulation that integrates policy learning, video prediction, and action evaluation using a shared video diffusion backbone. It shows superior performance on challenging long-horizon and fine-grained tasks.