Training Documents Reranker with Search Rubrics for Deep Research Agent

• URL: http://arxiv.org/abs/2608.03527v1
• Date: Tue, 04 Aug 2026 12:11:14 GMT
• Title: Training Documents Reranker with Search Rubrics for Deep Research Agent
• Abstract summary: We propose search-oriented rubrics that textitexplicitly define the requirements that high-quality document sets should satisfy.<n>We further train a document reranker textbfRubricRanker to select a high-quality subset from retrieved documents.
• Score: 54.08506174105984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval systems help deep research agents generate high-quality answers by providing relevant documents. However, existing retrievers typically select documents through relevance matching, while individually well-matched top-$k$ documents may not form a \textit{set} that satisfies the complex information needs of an agent query (\eg, diverse, concise and authoritative documents). In this paper, we propose search-oriented rubrics that \textit{explicitly} define the requirements that high-quality document sets should satisfy for each agent query. Our search rubrics are organized into a hierarchical structure and synthesized using a powerful LLM. Based on these search rubrics, we further train a document reranker \textbf{RubricRanker} to select a high-quality subset from retrieved documents. We design a two-stage training framework that consists of rubrics-guided supervised fine-tuning and rubric-based reinforcement learning. Extensive experiments demonstrate that RubricRanker outperforms the strongest baseline by 2.6 points on four deep research benchmarks and generalizes well to five RAG benchmarks.

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