SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data

2026-06-15

AI Summary

This work proposes a novel norm-based alignment paradigm that addresses the limitations of existing large language model alignment methods in effectively leveraging structured, dynamically updated, scenario-specific norms as training signals. For the first time, it treats structured model norms as the central objective of alignment and introduces a framework combining controllable norm instantiation with multi-agent adversarial synthesis to automatically generate fine-grained preference data distinguishing compliant from non-compliant behaviors. Experimental results across diverse norms and base models demonstrate that the proposed approach significantly enhances adherence to prescribed rules while preserving general capabilities and avoiding overly conservative responses.

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.

Problem

• specification-grounded alignment
• large language models
• model specifications
• alignment
• synthetic data

Innovation

• specification-grounded alignment
• synthetic data generation
• controllable instantiation
• multi-agent adversarial synthesis
• preference pairs