Jiaqing Liang
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The paper proposes ProRL, an effective Reinforcement Learning framework that rectifies gradient estimation deficiencies to optimize proactive recommendation paths, significantly outperforming existing state-of-the-art methods.
The paper proposes Deep Research as Rubric (DR-rubric), a novel evidence-driven framework that treats rubric construction itself as a research problem to generate fine-grained, scalable reward signals for open-ended reasoning tasks.
The paper introduces PlanRAG, a framework for Retrieval-Augmented Generation (RAG) that models exploratory reasoning problems as logical query trees, addressing representation and optimization gaps between structured SQL and unstructured natural language.
Papers
When RAG Meets Query Planning: Logical Query Trees for Resolving Exploratory Reasoning Problems
Ganlin Xu, Linghao Zhang, Zhitao Yin, Hongda Xi +6 more
The paper introduces PlanRAG, a framework for Retrieval-Augmented Generation (RAG) that models exploratory reasoning problems as logical query trees, addressing representation and optimization gaps be…