Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines
Quick Take
This survey reframes alignment tuning as a pipeline design problem, emphasizing data-centric approaches.
Key Points
- Decomposes alignment data construction into three stages.
- Identifies design trade-offs and failure modes in existing methods.
- Outlines open challenges for alignment data pipelines.
Article Excerpt
From source RSS / original summaryarXiv: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-offs and failure modes observed across prior alignment methods, and distill a set of high level principles that clarify how pipeline design choices influence the resulting optimization signal.
Finally, we outline open challenges for alignment data pipelines, including prompt-level alignment, agentic settings, and alignment under evolving objectives.
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