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StableReported 2026-10-02 12:00

Calibration-risk routing for controlled world-model adaptation

Researchers introduce the Model-Corrected World Model (MC-WM), a new approach in model-based reinforcement learning that addresses the model-selection problem in simulators by partitioning target data and using a confidence signal to adapt the model.

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Evidence

  • AarXiv cs.AIPrimary source2026-10-02 12:00
    Abstract: Model-based reinforcement learning (MBRL) can exploit simulated experience, but a simulator-to-target shift creates a model-selection problem: correcting the simulator and fitting the target directly can each fail under limited target data. We introduce the Model-Corrected World Model (MC-WM), which separates initial target data into disjoint fit, selection, and calibration partitions a…
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