1 citations · 1 across the 9 of their papers we have counts for
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InfoFlow: Reinforcing Search Agent Via Reward Density Optimization
Kun Luo, Hongjin Qian, Zheng Liu +5
Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic deep search. However, its application is often hindered by low \textbf{Reward De…
R3A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms
Xiaowei Yuan, Lei Jin, Haoxin Zhang +6
Retrieval-augmented generation (RAG) plays a critical role in user-generated content (UGC) platforms, but its effectiveness critically depends on accurate query-document relevance…
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate
Ziyang Huang, Wangtao Sun, Jun Zhao +1
This paper systematically addresses the challenges of rule retrieval, a crucial yet underexplored area. Vanilla retrieval methods using sparse or dense retrievers to directly searc…
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent
Ziyang Huang, Xiaowei Yuan, Yiming Ju +2
Retrieval-augmented generation (RAG) is a common strategy to reduce hallucinations in Large Language Models (LLMs). While reinforcement learning (RL) can enable LLMs to act as sear…
Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement
Xiaowei Yuan, Zhao Yang, Ziyang Huang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect context…
Capability Localization: Capabilities Can be Localized rather than Individual Knowledge
Xiusheng Huang, Jiaxiang Liu, Yequan Wang +2
Large scale language models have achieved superior performance in tasks related to natural language processing, however, it is still unclear how model parameters affect performance…