DyKnow-RAG uses Group Relative Policy Optimization with dual-group rollouts and posterior-driven advantage scaling to optimize context utilization in RAG for e-commerce relevance, showing offline gains and production lifts when deployed at Taobao.
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Pith papers citing it
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2025 2verdicts
UNVERDICTED 2representative citing papers
SHE is a new RL framework using stepwise hybrid examination rewards to improve reasoning quality and accuracy in large-scale e-commerce query-product relevance prediction.
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Learning to Trust: Dynamic Utilization of Retrieval-Augmented Generation for E-commerce Search Relevance
DyKnow-RAG uses Group Relative Policy Optimization with dual-group rollouts and posterior-driven advantage scaling to optimize context utilization in RAG for e-commerce relevance, showing offline gains and production lifts when deployed at Taobao.
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SHE: Stepwise Hybrid Examination Reinforcement Learning Framework for E-commerce Search Relevance
SHE is a new RL framework using stepwise hybrid examination rewards to improve reasoning quality and accuracy in large-scale e-commerce query-product relevance prediction.