SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
Wenjie Wang, Yue Huang, Zhengqing Yuan, Han Bao, Shiyi Du, Yuchen Ma, Yue Zhao, Yanfang Ye, Xiangliang Zhang
Why It Matters
What makes this one worth your time
Aligning LLMs with specific and evolving model specifications is crucial for ensuring compliance and adaptability in real-world applications.
SpecAlign aligns LLMs with specific model specifications using synthetic data.
Summary
The paper introduces SpecAlign, a framework for aligning large language models with provider-specific model specifications using synthetic data generated from specification documents.
Key contributions
- Introduction of specification-grounded alignment as a new paradigm.
- Development of SpecAlign framework for synthesizing alignment data from specifications.
- Demonstration of improved rule compliance without over-conservative behavior.
Notable insights
- The use of multi-agent adversarial data synthesis to generate preference pairs is a novel approach.
- Grounding alignment in explicit model specifications allows for scalable adaptation to policy changes.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.16276v2 Announce Type: replace 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.