arXiv:2605.26442v1 Announce Type: new Abstract: Much of the alignment tuning literature is organized around optimization objectives, while the construction of alignment data is often treated implicitly. In this survey, we adopt a data centric perspective and reframe alignment tuning as a pipeline design problem. We decompose alignment data construction into three interacting stages, response synthesis, preference evaluation, and preference instantiation, and use this framework to organize existing alignment methods into a unified taxonomy. Through this lens, we identify recurring design trade-
The rapid advancement and deployment of Large Language Models necessitate a deeper understanding and standardized approach to their alignment to ensure safety and effectiveness.
A data-centric perspective on AI alignment pipelines offers a methodical way to improve model behavior, impacting everything from AI safety to commercial viability and public trust.
The focus shifts from solely optimizing objectives to explicitly designing and refining the data pipelines that shape AI alignment, potentially standardizing development practices.
- · AI safety researchers
- · LLM developers
- · AI ethics and governance organizations
- · Enterprise AI adopters
- · Developers with ad-hoc alignment processes
- · AI systems with poor or biased alignment data
- · Companies reliant on black-box alignment
Improved methodologies for aligning large language models will lead to more reliable and controllable AI systems.
Standardized alignment data pipelines could become critical for regulatory compliance and AI product certification.
Enhanced alignment may accelerate the deployment of advanced AI agents in sensitive applications, increasing their societal impact and requiring new governance frameworks.
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