BoostStep: Boosting mathematical capability of Large Language Models via
  improved single-step reasoning
Cutting-edge large language models (LLMs) demonstrate promising performance
in solving complex math problems with a divide-and-conquer pipeline and the
assistance of in-context learning (ICL) examples. However, their potential for
improvement is limited by two critical problems within their ICL examples:
granularity-mismatch and the ensuing negative-effect noise problem.
Specifically, the LLMs are capable of the dividing process yet mostly failed by
inaccurate reasoning within a few conquer steps, while the ICL examples
retrieved in question-grained sometimes lack relevant steps for a specific
challenging reasoning step. Further, this disconnect may hinder the correct
reasoning due to its irrelevance. To this end, we focus on improving the
reasoning quality within each step and present BoostStep. BoostStep aligns the
granularity between the retrieving and reasoning on step grained, and provides
highly related ICL examples for each reasoning step with a novel `first-try'
strategy. BoostStep provides more relevant examples than the coarse
question-grained strategy, enhancing the model reasoning quality within each
step steadily. BoostStep is a general and robust reasoning-enhancing method
that not only improves standalone reasoning performance but also integrates
seamlessly with Monte Carlo Tree Search methods (MCTS) to refine both candidate
generation and decision-making. Quantitatively, it improves GPT-4o and
Qwen2.5-Math-72B by 3.6\% and 2.0\% respectively on various mathematical
benchmarks, and 7.5\% gain combined with MCTS.
677cdcd50604b68871999e0f
Beichen Zhang
admin_assigned
2025-01-07T09:37:59.472Z
64b93578ee257c3a4cfceed1
/avatars/e6188562254f75a09b4048b800860016.svg
Beichen Zhang
user
BeichenZhang
677cdcd50604b68871999e10
Yuhong Liu
677cdcd50604b68871999e11
Xiaoyi Dong
claimed_verified
2025-08-08T16:27:13.615Z
68943a6e8d3fb6db77ce2874
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/7LBz6WhQmiKxOqZByJhr9.jpeg
Xiaoyi Dong
user
LightDong
677cdcd50604b68871999e12
Yuhang Zang
claimed_verified
2025-01-07T08:41:16.697Z
63859cf3b2906edaf83af9f0
https://cdn-avatars.huggingface.co/v1/production/uploads/63859cf3b2906edaf83af9f0/kajwuVzd4pDucSPlwghxo.png
Yuhang Zang
user
yuhangzang
677cdcd50604b68871999e13
Pan Zhang
677cdcd50604b68871999e14
Haodong Duan
admin_assigned
2025-01-07T08:57:58.362Z
63ee1379190ddd6214efd73a
https://cdn-avatars.huggingface.co/v1/production/uploads/1676546883247-noauth.png
HAODONG DUAN
user
KennyUTC
677cdcd50604b68871999e15
Yuhang Cao
admin_assigned
2025-01-07T08:57:51.041Z
65000bef18830fabea469fdd
/avatars/b320c77dfad039d9f9c54127f610d44f.svg
Cao Yuhang
user
yhcao
677cdcd50604b68871999e16
Dahua Lin
admin_assigned
2025-01-07T08:56:41.557Z
636317ed80c1a705a6eff396
/avatars/3db090e101b916d9256d0d3e043db71d.svg
Dahua Lin
user
lindahua
677cdcd50604b68871999e17
Jiaqi Wang
admin_assigned
2025-01-07T09:37:28.234Z
64b4eec4faa3181a5eab9c46
/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
Jiaqi Wang
user
myownskyW7
BoostStep: Boosting mathematical capability of Large Language Models via
  improved single-step reasoning
Cutting-edge large language models (LLMs) demonstrate promising performance
in solving complex math problems with a divide-and-conquer pipeline and the
assistance of in-context learning (ICL) examples. However, their potential for
improvement is limited by two critical problems within their ICL examples:
granularity-mismatch and the ensuing negative-effect noise problem.
Specifically, the LLMs are capable of the dividing process yet mostly failed by
inaccurate reasoning within a few conquer steps, while the ICL examples
retrieved in question-grained sometimes lack relevant steps for a specific
challenging reasoning step. Further, this disconnect may hinder the correct
reasoning due to its irrelevance. To this end, we focus on improving the
reasoning quality within each step and present BoostStep. BoostStep aligns the
granularity between the retrieving and reasoning on step grained, and provides
highly related ICL examples for each reasoning step with a novel `first-try'
strategy. BoostStep provides more relevant examples than the coarse
question-grained strategy, enhancing the model reasoning quality within each
step steadily. BoostStep is a general and robust reasoning-enhancing method
that not only improves standalone reasoning performance but also integrates
seamlessly with Monte Carlo Tree Search methods (MCTS) to refine both candidate
generation and decision-making. Quantitatively, it improves GPT-4o and
Qwen2.5-Math-72B by 3.6\% and 2.0\% respectively on various mathematical
benchmarks, and 7.5\% gain combined with MCTS.
BoostStep improves large language models' reasoning quality in math problems by aligning granularity and providing relevant in-context learning examples, enhancing performance and integrating with Monte Carlo Tree Search methods.
large language models divide-and-conquer pipeline in-context learning granularity-mismatch negative-effect noise conquering steps question-grained step-grained reasoning quality first-try strategy Monte Carlo Tree Search GPT-4o Qwen2.5-Math-72B