Genuine embodied agency requires robots to turn continuous real-world experience into lasting, transferable skills. This demands continual learning that integrates new capabilities while preserving prior knowledge in evolving environments. Experience replay mitigates catastrophic forgetting, but storing complete demonstrations becomes costly as tasks accumulate. World-action models provide an alternative through generative replay. However, visually coherent rollouts may contain actions that cannot realize the predicted transitions, while new-task adaptation can disrupt learned behavior. To address these problems, we propose RIFAR (Reliability-Informed and Forgetting-Aware Replay), a reliability-informed and drift-aware generative replay framework. It reconstructs trajectories from compact historical demonstration prefixes and uses a trainable inverse-dynamics model to assess action-visual consistency. It first adapts to new tasks using current-task demonstrations and high-reliability replay, then evaluates how this adaptation changes action predictions on historical inputs and prioritizes reliable trajectories with greater action drift for further training. Across three LIBERO suites and real-world experiments, RIFAR outperforms the previous state of the art in WAM-based generative replay. On LIBERO-Goal, it achieves an AUC of 90.97 while retaining only 320 historical time steps per task, about 4.9% of those retained by 50-demonstration replay.

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