TY - GEN
T1 - Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution
AU - He, Tao
AU - Li, Hao
AU - Chen, Jingchang
AU - Liu, Runxuan
AU - Cao, Yixin
AU - Liao, Lizi
AU - Zheng, Zihao
AU - Chu, Zheng
AU - Liang, Jiafeng
AU - Liu, Ming
AU - Qin, Bing
N1 - Publisher Copyright:
© 2025 Association for Computational Linguistics.
PY - 2025
Y1 - 2025
N2 - The release of OpenAI's O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this paper aims to drive advancements in LLMs' reasoning abilities.
AB - The release of OpenAI's O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this paper aims to drive advancements in LLMs' reasoning abilities.
UR - https://www.scopus.com/pages/publications/105028554790
U2 - 10.18653/v1/2025.findings-acl.386
DO - 10.18653/v1/2025.findings-acl.386
M3 - 会议稿件
AN - SCOPUS:105028554790
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 7377
EP - 7417
BT - Findings of the Association for Computational Linguistics
A2 - Che, Wanxiang
A2 - Nabende, Joyce
A2 - Shutova, Ekaterina
A2 - Pilehvar, Mohammad Taher
PB - Association for Computational Linguistics (ACL)
T2 - 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025
Y2 - 27 July 2025 through 1 August 2025
ER -