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This work proposes SafePO, a reinforcement learning algorithm for refining violation identification while balancing explanatory reasoning and final verdicts, and develops AdaGuard, a family of 0.6B, 4B, and 8B guard models that assess agent trajectories under policies supplied at inference time.
Multi-UAV swarm coordination requires high-level task refinement across heterogeneous game-like operation segments with different controllers, risks, and time-dependent rewards. Existing hand-written refinement rules are difficult to tune when an intermediate action has no direct reward but changes downstream losses an...
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