SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
2026-06-15 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explain that as large language models are used more, it's important to follow specific rules set by providers rather than just general safety ideas. They introduce a new approach called specification-grounded alignment, which focuses on using these detailed rules directly to guide training. Their method, SpecAlign, creates training examples from the rule documents themselves to help models learn what behaviors are allowed and what breaks the rules. Tests show that this helps models follow rules better without losing other skills or becoming too cautious. Overall, the authors suggest this approach makes it easier to update models according to changing guidelines.
Large Language ModelsModel AlignmentSpecification-grounded alignmentSpecAlignTraining SignalsPreference PairsAdversarial Data SynthesisRule ComplianceModel SpecificationsBehavior Adaptation
Authors
Wenjie Wang, Yue Huang, Zhengqing Yuan, Han Bao, Shiyi Du, Yuchen Ma, Yue Zhao, Yanfang Ye, Xiangliang Zhang
Abstract
As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but instead by provider- or application-specific model specifications. These specifications are typically long, structured, and frequently updated, yet existing alignment pipelines lack a systematic mechanism to operationalize them as training signals. In this paper, we propose specification-grounded alignment, a new alignment paradigm that treats provider-authored model specifications as the primary alignment target rather than abstract principles or static benchmarks. To instantiate this paradigm, we introduce SpecAlign, a framework that synthesizes alignment data directly from specification documents. SpecAlign combines structured rule annotation, controllable specification instantiation, and multi-agent adversarial data synthesis to generate fine-grained, boundary-aware preference pairs that capture both compliant behaviors and meaningful specification violations. Experiments across multiple model specifications and backbone models demonstrate that training with SpecAlign consistently improves rule compliance while preserving general capabilities and avoiding over-conservative behavior. These results suggest that grounding alignment in explicit model specifications enables rapid, precise, and scalable adaptation of LLM behavior to evolving policy requirements.