Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines
Quick Answer
This paper shows that This survey reframes alignment tuning for large language models as a pipeline design problem, highlighting three stages: response synthesis, preference evaluation, and preference instantiation.
Quick Take
This survey reframes alignment tuning for large language models as a pipeline design problem, highlighting three stages: response synthesis, preference evaluation, and preference instantiation. It identifies design trade-offs and principles that affect optimization signals, while outlining challenges like prompt-level alignment and evolving objectives.
Key Points
- Aligns tuning literature with a data-centric perspective on alignment data construction.
- Decomposes alignment data into response synthesis, preference evaluation, and instantiation.
- Identifies recurring design trade-offs in existing alignment methods.
- Distills principles clarifying the impact of pipeline design on optimization signals.
- Outlines open challenges in alignment data pipelines for evolving objectives.
Paper Resources
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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