Which Data Attributes Stimulate Math and Code Reasoning? An
  Investigation via Influence Functions
Large language models (LLMs) have demonstrated remarkable reasoning
capabilities in math and coding, often bolstered by post-training on the
chain-of-thoughts (CoTs) generated by stronger models. However, existing
strategies for curating such training data predominantly rely on heuristics,
limiting generalizability and failing to capture subtleties underlying in data.
To address these limitations, we leverage influence functions to systematically
attribute LLMs' reasoning ability on math and coding to individual training
examples, sequences, and tokens, enabling deeper insights into effective data
characteristics. Our Influence-based Reasoning Attribution (Infra) uncovers
nontrivial cross-domain effects across math and coding tasks: high-difficulty
math examples improve both math and code reasoning, while low-difficulty code
tasks most effectively benefit code reasoning. Based on these findings, we
introduce a simple yet effective dataset reweighting strategy by flipping task
difficulty, which doubles AIME24 accuracy from 10\% to 20\% and boosts
LiveCodeBench accuracy from 33.8\% to 35.3\% for Qwen2.5-7B-Instruct. Moreover,
our fine-grained attribution reveals that the sequence-level exploratory
behaviors enhance reasoning performance in both math and code, and the
token-level influence patterns are distinct for math and code reasoning: the
former prefers natural language logic connectors and the latter emphasizes
structural syntax.
68352aac38e5ca9eb5349c2f
Siqi Kou
admin_assigned
2025-05-27T13:05:20.747Z
654e330f350abceb30a1390b
/avatars/e54a8be788fa1bdc7acefecc208215bb.svg
KouSiqi
user
karrykkk
68352aac38e5ca9eb5349c30
Qingyuan Tian
68352aac38e5ca9eb5349c31
Hanwen Xu
68352aac38e5ca9eb5349c32
Zihao Zeng
claimed_verified
2025-05-29T07:44:07.147Z
6544c0585a13979f82038a1c
/avatars/01f3e862d49020e9eaf1728e4ba97bea.svg
Zeng Zihao
user
zzh6666
68352aac38e5ca9eb5349c33
Zhijie Deng
Which Data Attributes Stimulate Math and Code Reasoning? An
  Investigation via Influence Functions
Large language models (LLMs) have demonstrated remarkable reasoning
capabilities in math and coding, often bolstered by post-training on the
chain-of-thoughts (CoTs) generated by stronger models. However, existing
strategies for curating such training data predominantly rely on heuristics,
limiting generalizability and failing to capture subtleties underlying in data.
To address these limitations, we leverage influence functions to systematically
attribute LLMs' reasoning ability on math and coding to individual training
examples, sequences, and tokens, enabling deeper insights into effective data
characteristics. Our Influence-based Reasoning Attribution (Infra) uncovers
nontrivial cross-domain effects across math and coding tasks: high-difficulty
math examples improve both math and code reasoning, while low-difficulty code
tasks most effectively benefit code reasoning. Based on these findings, we
introduce a simple yet effective dataset reweighting strategy by flipping task
difficulty, which doubles AIME24 accuracy from 10\% to 20\% and boosts
LiveCodeBench accuracy from 33.8\% to 35.3\% for Qwen2.5-7B-Instruct. Moreover,
our fine-grained attribution reveals that the sequence-level exploratory
behaviors enhance reasoning performance in both math and code, and the
token-level influence patterns are distinct for math and code reasoning: the
former prefers natural language logic connectors and the latter emphasizes
structural syntax.
Influence functions are used to attribute LLMs' reasoning in math and coding to individual training elements, revealing cross-domain effects and enabling a reweighting strategy that improves model accuracy.
Large language models (LLMs) chain-of-thoughts (CoTs) influence functions attribution data curation reasoning ability high-difficulty math examples low-difficulty code tasks dataset reweighting strategy AIME24 accuracy LiveCodeBench accuracy sequence-level exploratory behaviors token-level influence patterns natural language logic connectors structural syntax parameter-efficient fine-tuning