A mathematically justified local-global metric regularizer converts approximately optimized empirical world models into provably non-collapsed encoders with controlled planning transfer for deterministic nonlinear control.
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Metric Non-Collapse in Learned World Models for Control: Approximation Theory, Finite-Sample Geometric Guarantees, and Deterministic Planning Transfer
A mathematically justified local-global metric regularizer converts approximately optimized empirical world models into provably non-collapsed encoders with controlled planning transfer for deterministic nonlinear control.