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Regularized importance sampling in LLM RL breaks reward shift invariance, but a covariance-based estimand recovers this property, with distinct centering strategies impacting bias and variance.
Leading multivariate TSFMs fail basic "unit tests" for temporal logic, underutilizing simple trends and leading indicators even with exogenous covariates.