ReZero: Enhancing LLM search ability by trying one-more-time
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.
67ff4bdb1dc5d56fdd7a1bc4
Alan Dao
extracted_confirmed
2025-04-16T11:13:40.678Z
62d7b2339b629105a5d6888a
https://cdn-avatars.huggingface.co/v1/production/uploads/62d7b2339b629105a5d6888a/pYi994fqzXctgoed79oUo.png
Alan Dao
user
alandao
67ff4bdb1dc5d56fdd7a1bc5
Thinh Le
extracted_pending
2025-08-25T09:46:31.101Z
68ac30a8fce72207c7bdf610
/avatars/0f7c16807b1b946fe745ab110aa9ad0f.svg
Thinh Le
user
thinh-menlo
ReZero: Enhancing LLM search ability by trying one-more-time
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.
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.
Retrieval-Augmented Generation Large Language Model reinforcement learning search query retry query formulation reasoning accuracy robustness information-seeking scenarios