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

ACL ARR 2026 January Submission2729 Authors

Keywords: alignment tax, model merging, instruction tuning, calibration

Abstract: 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 show 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.

Paper Type: Short

Research Area: Safety and Alignment in LLMs

Research Area Keywords: safety and alignment, calibration/uncertainty, fine-tuning, robustness

Languages Studied: English

Submission Number: 2729