arXiv AI
PAVE: Predictive Alignment and Value-Guided Evolution for World-Action Policies
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The paper introduces a policy‑aligned predictive verifier (PAV) that evaluates candidate action prefixes from vision‑language‑action (VLA) policies to determine how far to execute before re‑planning. PAV predicts the consequences of prefixes and checks their compatibility with successful task completion, using trajectory‑level survival supervision and a localization prior to learn local judgments. Experiments on LIBERO, RoboTwin 2.0, and real‑world manipulation demonstrate that PAV improves the trade‑off between success rate and query cost compared to existing adaptive methods.
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DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies
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arXiv AI
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EmbodiedSkills: World Action Model-augmented Cascaded Skills for Vision-Language-Action Agent
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