Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging

The "alignment tax" of post-training is typically framed as a drop in task accuracy.We show it also involves a severe loss of calibration, making models overconfident, less reliable, and model outputs less diverse.We demonstrate that this trade-off can be navigated effectively via a simple post-hoc intervention: interpolating between a model's weights before and after alignment.Crucially, this is not a strict trade-off.We find that the process consistently reveals Pareto-optimal interpolations-models that improve accuracy beyond both parents while substantially recovering the calibration lost during alignment.Our work demonstrates that simple model merging provides a computationally efficient method for mitigating the full scope of the alignment tax, yielding models that are more capable and more reliable.1