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IADD-TR reveals that decoupling action dynamics from environmental evolution can drastically enhance sample efficiency in model-based reinforcement learning.
CDFM outperforms traditional causal discovery algorithms by leveraging a unified framework that adapts to diverse datasets without the need for extensive retraining.
