Winning Comparisons, Losing Predictions: Comparison Feedback Misleads World Models

Held-out comparison loss can select checkpoints that predict observed outcomes less well. In a separately frozen Delta-IRIS/Crafter study with correct nearest-candidate labels, feedback-selected checkpoints have worse covered-target likelihood than fixed-500 endpoints in both principal pairwise arms. A retrospective reading of all twelve pairwise validation trajectories finds their feedback minima at the covered-NLL grid maxima. The larger relative benefit of balancing at the selected endpoint accompanies larger sampled-model error, not restored calibration. We explain why feedback fitting and outcome prediction are distinct through specified acquisition and scoring laws. Exact single-row analyses retain multiplicities, ties and finite-batch normalization, separating the pairwise stationary target from winner-channel calibration and outcome identification. An identifying control links objective mismatch to an action error. Assigned multiplicity interventions shift a majority of active covered-target probabilities in the predicted direction across released IRIS and Delta-IRIS checkpoints, while normalization sharply attenuates the Breakout magnitude and both fixed-500 Delta tests fail. Channel's unchanged checkpoint has better held-out feedback loss than every fitted candidate on its recorded grid. These results show how comparison-interface choices and checkpoint selection can diverge from predictive quality, with explicit boundaries between population laws and finite neural training.