Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging
arXiv:2510.17426v3 Announce Type: replace 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 w
The paper addresses a core challenge in aligning large language models, a rapidly developing area, as models become more integrated into critical applications.
This research offers a method to mitigate the 'alignment tax' which hinders AI performance and reliability, directly impacting the practical utility and trustworthiness of advanced AI systems.
The ability to achieve Pareto-optimal interpolations between pre- and post-alignment model weights suggests a path to improving both accuracy and calibration, rather than having to choose one over the other.
- · AI developers
- · AI-powered services
- · End-users of AI models
- · Developers relying on strict trade-offs
- · Models with poor alignment mechanisms
AI models become more reliable and trustworthy due to improved calibration without sacrificing task performance.
Increased adoption and deployment of advanced AI systems in sensitive domains where reliability is paramount.
Accelerated development of AI agents and autonomous systems as calibration and accuracy improve concurrently.
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