Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence
Rubrics provide structured, fine-grained signals for training and evaluating large language models (LLMs). Yet reliable query-specific rubrics are difficult to construct. Existing approaches often derive supervision from human-written rubrics, preference data, or sampled responses. Direct query-to-rubric generation avoids these resources, but provides no explicit check that a plausible rubric is useful. Such a rubric may fail to distinguish answer quality, reward an optional style, or penalize a valid alternative strategy. We introduce Rubrics on Trial, a query-only framework that evolves a ru
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- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Haocheng Yang →
“Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence”
- LinkedLinked via arxiv author · 85%Licheng Pan →
“Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence”
- LinkedLinked via arxiv author · 85%Xiaoxi Li →
“Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence”
- LinkedLinked via arxiv author · 85%Zhichao Chen →
“Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence”
- LinkedLinked via arxiv author · 85%Zhiheng Zhang →
“Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence”
- LinkedLinked via arxiv author · 85%Yuan Lu →
“Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence”