@inproceedings{bu-etal-2025-enhanced,
title = "Enhanced Data Synthesis for {LLM} through Reasoning Structures Generated by Hierarchical {GF}low{N}et",
author = "Bu, Tianpeng and
Zhang, Minying and
Duan, Hongtao and
Li, Shurui and
Hu, Lulu and
Li, Yu",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.821/",
doi = "10.18653/v1/2025.findings-acl.821",
pages = "15931--15958",
ISBN = "979-8-89176-256-5",
abstract = "Large language models (LLMs) excel in problem-solving but require training data with diverse reasoning processes. Existing methods mainly optimize instruction-response pairs but lack a systematic design for the underlying reasoning structure. This paper proposes RSS: a Reasoning Structure driven data Synthesis method. We first proactively develop a hierarchical GFlowNet to construct reasoning structures efficiently through a coarse-to-fine directed acyclic graph (DAG) growth process. Then reasoning DAGs are leveraged to actively guide the instruction generation via an iterative suggester-editor workflow and enhance response quality using a structure-aware strategy. Experiments show that LLMs trained on our synthetic datasets achieve 48.50{\%}, 84.00{\%}, 79.90{\%} for AlpacaEval2, GSM8K and HumanEval, outperforming existing data synthesis methods."
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%0 Conference Proceedings
%T Enhanced Data Synthesis for LLM through Reasoning Structures Generated by Hierarchical GFlowNet
%A Bu, Tianpeng
%A Zhang, Minying
%A Duan, Hongtao
%A Li, Shurui
%A Hu, Lulu
%A Li, Yu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F bu-etal-2025-enhanced
%X Large language models (LLMs) excel in problem-solving but require training data with diverse reasoning processes. Existing methods mainly optimize instruction-response pairs but lack a systematic design for the underlying reasoning structure. This paper proposes RSS: a Reasoning Structure driven data Synthesis method. We first proactively develop a hierarchical GFlowNet to construct reasoning structures efficiently through a coarse-to-fine directed acyclic graph (DAG) growth process. Then reasoning DAGs are leveraged to actively guide the instruction generation via an iterative suggester-editor workflow and enhance response quality using a structure-aware strategy. Experiments show that LLMs trained on our synthetic datasets achieve 48.50%, 84.00%, 79.90% for AlpacaEval2, GSM8K and HumanEval, outperforming existing data synthesis methods.
%R 10.18653/v1/2025.findings-acl.821
%U https://aclanthology.org/2025.findings-acl.821/
%U https://doi.org/10.18653/v1/2025.findings-acl.821
%P 15931-15958
Markdown (Informal)
[Enhanced Data Synthesis for LLM through Reasoning Structures Generated by Hierarchical GFlowNet](https://aclanthology.org/2025.findings-acl.821/) (Bu et al., Findings 2025)
ACL