title: Reinforced Internal-External Knowledge Synergistic Reasoning for
    Efficient Adaptive Search Agent
summary: 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 search agents by activating retrieval
  capabilities, existing ones often underutilize their internal knowledge. This
  can lead to redundant retrievals, potential harmful knowledge conflicts, and
  increased inference latency. To address these limitations, an efficient and
  adaptive search agent capable of discerning optimal retrieval timing and
  synergistically integrating parametric (internal) and retrieved (external)
  knowledge is in urgent need. This paper introduces the Reinforced
  Internal-External Knowledge Synergistic Reasoning Agent (IKEA), which could
  indentify its own knowledge boundary and prioritize the utilization of internal
  knowledge, resorting to external search only when internal knowledge is deemed
  insufficient. This is achieved using a novel knowledge-boundary aware reward
  function and a knowledge-boundary aware training dataset. These are designed
  for internal-external knowledge synergy oriented RL, incentivizing the model to
  deliver accurate answers, minimize unnecessary retrievals, and encourage
  appropriate external searches when its own knowledge is lacking. Evaluations
  across multiple knowledge reasoning tasks demonstrate that IKEA significantly
  outperforms baseline methods, reduces retrieval frequency significantly, and
  exhibits robust generalization capabilities.
paper_authors._id: 6822ba9e4867f1e9dc14c294
paper_authors.name: Ziyang Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T08:11:42.410Z
paper_authors._id: 616648c84c0937d31946f21b
paper_authors.avatarUrl: /avatars/7ca27de5c5116c91ff1db61ba6277ed5.svg
paper_authors.fullname: Ziyang
paper_authors.type: user
paper_authors.user: hzy
paper_authors._id: 6822ba9e4867f1e9dc14c295
paper_authors.name: Xiaowei Yuan
paper_authors._id: 6822ba9e4867f1e9dc14c296
paper_authors.name: Yiming Ju
paper_authors._id: 6822ba9e4867f1e9dc14c297
paper_authors.name: Jun Zhao
paper_authors._id: 6822ba9e4867f1e9dc14c298
paper_authors.name: Kang Liu
paper_title: Reinforced Internal-External Knowledge Synergistic Reasoning for
    Efficient Adaptive Search Agent
paper_summary: 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 search agents by activating retrieval
  capabilities, existing ones often underutilize their internal knowledge. This
  can lead to redundant retrievals, potential harmful knowledge conflicts, and
  increased inference latency. To address these limitations, an efficient and
  adaptive search agent capable of discerning optimal retrieval timing and
  synergistically integrating parametric (internal) and retrieved (external)
  knowledge is in urgent need. This paper introduces the Reinforced
  Internal-External Knowledge Synergistic Reasoning Agent (IKEA), which could
  indentify its own knowledge boundary and prioritize the utilization of internal
  knowledge, resorting to external search only when internal knowledge is deemed
  insufficient. This is achieved using a novel knowledge-boundary aware reward
  function and a knowledge-boundary aware training dataset. These are designed
  for internal-external knowledge synergy oriented RL, incentivizing the model to
  deliver accurate answers, minimize unnecessary retrievals, and encourage
  appropriate external searches when its own knowledge is lacking. Evaluations
  across multiple knowledge reasoning tasks demonstrate that IKEA significantly
  outperforms baseline methods, reduces retrieval frequency significantly, and
  exhibits robust generalization capabilities.
paper_ai_summary: The IKEA agent optimizes reinforcement learning in LLMs by synergistically using internal and external knowledge, reducing hallucinations and unnecessary retrievals.
paper_ai_keywords: Retrieval-augmented generation Large Language Models reinforcement learning search agent internal knowledge external knowledge knowledge-boundary aware reward function knowledge-boundary aware training dataset internal-external knowledge synergy