Preference Alignment Helps Probabilistic Forecasts Only When the Decision-Maker Cannot Adapt

Preference alignment has been proposed as a way to make forecasts serve downstream decisions, but it is not known when, or whether, distorting a forecast can actually improve a decision. We settle this question for probabilistic forecasting, and the answer is sharply two-sided. If the decision-maker is adaptive, choosing the action that minimizes expected cost under the reported distribution, then reporting the true conditional distribution is optimal and no aligned forecast can ever do better. For newsvendor-type decisions, which cover the standard motivating examples of safety stock and reserve sizing, this is exact: minimizing expected cost and querying the critical-ratio quantile are the same operation, so calibration already suffices. If instead the decision-maker is non-adaptive, applying a fixed readout it cannot re-tune to the cost structure (most commonly a system that simply consumes the point forecast), then the cost-minimizing report is provably miscalibrated, and alignment has real value. We instantiate the positive regime with Prob-TPO, which aligns full quantile functions with a distributional DPO objective, and confirm both predictions on four benchmarks (energy, retail, transformer temperature, electricity load; 5 seeds, out-of-sample). Under a fixed point-forecast readout, Prob-TPO reduces realized newsvendor cost by 6–43% relative to calibrated quantile regression, closing up to 92% of the gap to a utility-optimal oracle, while an adaptive consumer sees no benefit, exactly as the theory predicts. We further show the auxiliary and calibration weights control how much of this gain is captured, and that alignment silently breaks quantile-label semantics: an aligned “95th percentile” carries 99% coverage, so downstream consumers must re-map labels.