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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…
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…
Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models
Yifan Wei, Xiaoyan Yu, Yixuan Weng +4
Large language models encapsulate knowledge and have demonstrated superior performance on various natural language processing tasks. Recent studies have localized this knowledge to…