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Production multi-agent systems replace agents constantly, on the assumption that an agent filling a role is interchangeable with any other agent that can do the job, and agents are more fungible in task outcome than in coordination efficiency, with larger swap effects after longer formation histories.
MirrorCraft is introduced, a paired benchmark for evaluating agents under hidden rule changes in Minecraft and provides a controlled setting for studying how agents use gameplay outcomes when the rules of the current world differ from familiar ones.
This work proposes $O^2-CritiCuRL, a novel curriculum reinforcement learning framework that introduces critical-step awareness through an iterative offline-online paradigm, and employs a progressive step-level reinforcement learning strategy, where truncated chains guide the model to infer missing steps and refine its...
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