title: ReZero: Enhancing LLM search ability by trying one-more-time
summary: Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM)
  performance on knowledge-intensive tasks but depends heavily on initial search
  query quality. Current methods, often using Reinforcement Learning (RL),
  typically focus on query formulation or reasoning over results, without
  explicitly encouraging persistence after a failed search. We introduce ReZero
  (Retry-Zero), a novel RL framework that directly rewards the act of retrying a
  search query following an initial unsuccessful attempt. This incentivizes the
  LLM to explore alternative queries rather than prematurely halting. ReZero
  demonstrates significant improvement, achieving 46.88% accuracy compared to a
  25% baseline. By rewarding persistence, ReZero enhances LLM robustness in
  complex information-seeking scenarios where initial queries may prove
  insufficient.
paper_authors._id: 67ff4bdb1dc5d56fdd7a1bc4
paper_authors.name: Alan Dao
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-16T11:13:40.678Z
paper_authors._id: 62d7b2339b629105a5d6888a
paper_authors.avatarUrl: /avatars/c3f164fde6b8f9a671890e08ce8a3e75.svg
paper_authors.fullname: Alan Dao
paper_authors.type: user
paper_authors.user: alandao
paper_authors._id: 67ff4bdb1dc5d56fdd7a1bc5
paper_authors.name: Thinh Le
paper_title: ReZero: Enhancing LLM search ability by trying one-more-time
paper_summary: Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM)
  performance on knowledge-intensive tasks but depends heavily on initial search
  query quality. Current methods, often using Reinforcement Learning (RL),
  typically focus on query formulation or reasoning over results, without
  explicitly encouraging persistence after a failed search. We introduce ReZero
  (Retry-Zero), a novel RL framework that directly rewards the act of retrying a
  search query following an initial unsuccessful attempt. This incentivizes the
  LLM to explore alternative queries rather than prematurely halting. ReZero
  demonstrates significant improvement, achieving 46.88% accuracy compared to a
  25% baseline. By rewarding persistence, ReZero enhances LLM robustness in
  complex information-seeking scenarios where initial queries may prove
  insufficient.
paper_ai_summary: ReZero, a reinforcement learning framework, enhances the performance of Retrieval-Augmented Generation by rewarding the persistence of search retries after initial failures, improving accuracy in knowledge-intensive tasks.
paper_ai_keywords: Retrieval-Augmented Generation Large Language Model reinforcement learning search query retry query formulation reasoning accuracy robustness information-seeking scenarios