title: ZeroSearch: Incentivize the Search Capability of LLMs without Searching
summary: Effective information searching is essential for enhancing the reasoning and
  generation capabilities of large language models (LLMs). Recent research has
  explored using reinforcement learning (RL) to improve LLMs' search capabilities
  by interacting with live search engines in real-world environments. While these
  approaches show promising results, they face two major challenges: (1)
  Uncontrolled Document Quality: The quality of documents returned by search
  engines is often unpredictable, introducing noise and instability into the
  training process. (2) Prohibitively High API Costs: RL training requires
  frequent rollouts, potentially involving hundreds of thousands of search
  requests, which incur substantial API expenses and severely constrain
  scalability. To address these challenges, we introduce ZeroSearch, a
  reinforcement learning framework that incentivizes the search capabilities of
  LLMs without interacting with real search engines. Our approach begins with
  lightweight supervised fine-tuning to transform the LLM into a retrieval module
  capable of generating both relevant and noisy documents in response to a query.
  During RL training, we employ a curriculum-based rollout strategy that
  incrementally degrades the quality of generated documents, progressively
  eliciting the model's reasoning ability by exposing it to increasingly
  challenging retrieval scenarios. Extensive experiments demonstrate that
  ZeroSearch effectively incentivizes the search capabilities of LLMs using a 3B
  LLM as the retrieval module. Remarkably, a 7B retrieval module achieves
  comparable performance to the real search engine, while a 14B retrieval module
  even surpasses it. Furthermore, it generalizes well across both base and
  instruction-tuned models of various parameter sizes and is compatible with a
  wide range of RL algorithms.
paper_authors._id: 681c15ab84d0a008fcdb1ee8
paper_authors.name: Hao Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T07:22:41.387Z
paper_authors._id: 662cef87bf294785d1f45c75
paper_authors.avatarUrl: /avatars/15c253ae955c86ad2b459b93edbe694b.svg
paper_authors.fullname: hao sun
paper_authors.type: user
paper_authors.user: sunhaonlp
paper_authors._id: 681c15ab84d0a008fcdb1ee9
paper_authors.name: Zile Qiao
paper_authors._id: 681c15ab84d0a008fcdb1eea
paper_authors.name: Jiayan Guo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-08T08:59:01.834Z
paper_authors._id: 66224557c61c7fbd98099079
paper_authors.avatarUrl: /avatars/a4f2144585c808865c73b5b7f0087c1f.svg
paper_authors.fullname: GJ
paper_authors.type: user
paper_authors.user: SpaceProduct
paper_authors._id: 681c15ab84d0a008fcdb1eeb
paper_authors.name: Xuanbo Fan
paper_authors._id: 681c15ab84d0a008fcdb1eec
paper_authors.name: Yingyan Hou
paper_authors._id: 681c15ab84d0a008fcdb1eed
paper_authors.name: Yong Jiang
paper_authors._id: 681c15ab84d0a008fcdb1eee
paper_authors.name: Pengjun Xie
paper_authors._id: 681c15ab84d0a008fcdb1eef
paper_authors.name: Fei Huang
paper_authors._id: 681c15ab84d0a008fcdb1ef0
paper_authors.name: Yan Zhang
paper_title: ZeroSearch: Incentivize the Search Capability of LLMs without Searching
paper_summary: Effective information searching is essential for enhancing the reasoning and
  generation capabilities of large language models (LLMs). Recent research has
  explored using reinforcement learning (RL) to improve LLMs' search capabilities
  by interacting with live search engines in real-world environments. While these
  approaches show promising results, they face two major challenges: (1)
  Uncontrolled Document Quality: The quality of documents returned by search
  engines is often unpredictable, introducing noise and instability into the
  training process. (2) Prohibitively High API Costs: RL training requires
  frequent rollouts, potentially involving hundreds of thousands of search
  requests, which incur substantial API expenses and severely constrain
  scalability. To address these challenges, we introduce ZeroSearch, a
  reinforcement learning framework that incentivizes the search capabilities of
  LLMs without interacting with real search engines. Our approach begins with
  lightweight supervised fine-tuning to transform the LLM into a retrieval module
  capable of generating both relevant and noisy documents in response to a query.
  During RL training, we employ a curriculum-based rollout strategy that
  incrementally degrades the quality of generated documents, progressively
  eliciting the model's reasoning ability by exposing it to increasingly
  challenging retrieval scenarios. Extensive experiments demonstrate that
  ZeroSearch effectively incentivizes the search capabilities of LLMs using a 3B
  LLM as the retrieval module. Remarkably, a 7B retrieval module achieves
  comparable performance to the real search engine, while a 14B retrieval module
  even surpasses it. Furthermore, it generalizes well across both base and
  instruction-tuned models of various parameter sizes and is compatible with a
  wide range of RL algorithms.
paper_ai_summary: ZeroSearch is a reinforcement learning framework that improves LLM search capabilities without using real search engines, using supervised fine-tuning and curriculum-based rollouts to handle document quality and API cost challenges.
paper_ai_keywords: reinforcement learning LLMs search capabilities document quality API costs lightweight supervised fine-tuning retrieval module curriculum-based rollout strategy varying parameter sizes RL algorithms