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
/avatars/c3f164fde6b8f9a671890e08ce8a3e75.svg
Alan Dao
user
alandao
67ff4bdb1dc5d56fdd7a1bc5
Thinh Le
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.
Retrieval-Augmented Generation (RAG) Large Language Model (LLM) knowledge-intensive tasks Reinforcement Learning (RL) query formulation reasoning over results ReZero (Retry-Zero) persistence search query unsuccessful attempt alternative queries robustness information-seeking scenarios