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Dense retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize retrieval metrics or downstream task performance. To address this problem, we introduce RELER (REinforcement LEarning for Retrieval), a reinforcement learning framework that enables...
Open-ended query generation lacks standard answers, thus necessitating an effective reward mechanism. Pointwise scoring rubrics provide limited information about the relative quality of sample answers under the same prompt; merging multiple rubric judgments into a single score may also mask the differences between thes...
RankBuffer is introduced, which maintains an ordered, query-specific buffer of previously judged responses as a reusable quality scale that achieves nearly on-par performance with the strongest ranking-based reward baseline while substantially reducing judging cost.
By systematically characterizing the search behavior and failure modes of long-horizon search agents, this work points to practical directions for building better deep research systems, including stronger query formulation, more effective evidence selection and context management, and stopping criteria based on whether...
Fetch-then-Explore is proposed, which separates page selection from evidence extraction and keeps what it selects: pages are recorded in a per-question workspace on the filesystem rather than the context window or a transient session, and evidence is pulled from them on demand later.
Experiments across multiple dense retrievers show that fine-tuning with MCP-Focus-enhanced documents consistently improves retrieval effectiveness over baseline document methods on multiple benchmarks.
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