Value-Aware Training under Two Protected Dynamics Changes
Abstract
Value-aware learning emphasizes consequential model errors, while dual robustness separates shifts during learning from perturbations during use. Evaluation should vary those mechanisms independently and inspect support in regions the learned policy visits. A low average model loss cannot establish both protections, and a robust objective remains tied to its declared uncertainty descriptions. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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