Which Pairs to Compare for LLM Post-Training?
Preference-based post-training has become a central paradigm for aligning language models. A common data-collection strategy is to generate a small set of completions for each prompt and label the resulting comparison pairs. However, human preference labels are often much more expensive than generating additional completions, suggesting a different use of the same labeling budget: generate a larger pool of completions, but label only the most informative comparison pairs. This paper studies which pairs should be compared in preference-based post-training. We formulate comparison curation as a sampling-design problem and evaluate designs by the quality of the final policy under the prefere...
| Authors | Jiangze Han, Vineet Goyal, Will Ma |
|---|---|
| Primary category | cs.AI |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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