LiteSearch: Efficacious Tree Search for LLM
Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree
Search) can dramatically boost LLM performance on complex mathematical
reasoning tasks. However, they often require more than 10 times the
computational resources of greedy decoding due to wasteful search strategies,
making them difficult to be deployed in practical applications. This study
introduces a novel guided tree search algorithm with dynamic node selection and
node-level exploration budget (maximum number of children) calculation to
tackle this issue. By considering the search progress towards the final answer
(history) and the guidance from a value network (future) trained without any
step-wise annotations, our algorithm iteratively selects the most promising
tree node before expanding it within the boundaries of the allocated
computational budget. Experiments conducted on the GSM8K and TabMWP datasets
demonstrate that our approach not only offers competitive performance but also
enjoys significantly lower computational costs compared to baseline methods.
668371213d01a30be915a776
Ante Wang
claimed_verified
2025-02-18T09:37:14.804Z
657814faa87010c9f87cc6d4
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Ante Wang
user
xmu-nlp
668371213d01a30be915a777
Linfeng Song
admin_assigned
2024-07-03T09:19:37.386Z
64c94eddcb2f1bf0e7db5a4d
/avatars/f7e2532d3c85d5e5b5a02c579ea68c3a.svg
Linfeng Song
user
freesunshine0316
668371213d01a30be915a778
Ye Tian
claimed_verified
2024-10-17T11:29:08.511Z
6414061f996b2e426f23df48
/avatars/db0af49c5f017f7c455a076b8b2212b6.svg
Ye Tian
user
yetian
668371213d01a30be915a779
Baolin Peng
admin_assigned
2024-07-03T09:25:52.770Z
61942296d5c2ba6daa290357
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Baolin Peng
user
Baolin
668371213d01a30be915a77a
Dian Yu
claimed_verified
2024-07-03T07:41:10.324Z
62d58fd53bf5e059f7cc3245
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Dian Yu
user
yudian
668371213d01a30be915a77b
Haitao Mi
admin_assigned
2024-07-03T09:26:00.192Z
65147a1426fbd558dbd08f1b
/avatars/86574ee2d5c22e940be1c4e50be88675.svg
Haitao Mi
user
haitaominlp
668371213d01a30be915a77c
Jinsong Su
668371213d01a30be915a77d
Dong Yu
LiteSearch: Efficacious Tree Search for LLM
Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree
Search) can dramatically boost LLM performance on complex mathematical
reasoning tasks. However, they often require more than 10 times the
computational resources of greedy decoding due to wasteful search strategies,
making them difficult to be deployed in practical applications. This study
introduces a novel guided tree search algorithm with dynamic node selection and
node-level exploration budget (maximum number of children) calculation to
tackle this issue. By considering the search progress towards the final answer
(history) and the guidance from a value network (future) trained without any
step-wise annotations, our algorithm iteratively selects the most promising
tree node before expanding it within the boundaries of the allocated
computational budget. Experiments conducted on the GSM8K and TabMWP datasets
demonstrate that our approach not only offers competitive performance but also
enjoys significantly lower computational costs compared to baseline methods.
A guided tree search algorithm with dynamic node selection and budget calculation improves LLM mathematical reasoning performance while reducing computational costs.
Monte Carlo Tree Search tree search algorithms LLM guided tree search dynamic node selection node-level exploration budget value network GSM8K TabMWP