SpecAlign: Efficient specification-grounded alignment of large language models via synthetic data

Researchers propose the specification-grounded alignment paradigm and the SpecAlign framework, which take specifications written by model providers, rather than abstract principles or static benchmarks, as the primary target of alignment. SpecAlign synthesizes alignment data directly from specification documents, combining structured rule annotation, controllable specification instantiation, and multi-agent adversarial data synthesis to generate fine-grained, boundary-aware preference pairs that cover both compliant behavior and meaningful specification violations. Experiments on multiple model specifications and multiple base models show that training with SpecAlign consistently improves rule compliance while preserving general capabilities and avoiding overly conservative behavior. The authors state that these results show that alignment grounded in explicit model specifications can enable model behavior to adapt quickly, precisely, and scalably to continuously updated policy requirements.