Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning Article Swipe

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Haiming Wang , Mert Unsal , Xiaohan Lin , Mantas Baksys , Junqi Liu , Marco Dos Santos , Flood Sung , Marina Vinyes , Zhenzhe Ying , Ziyue Zhu , Jianqiao Lu , Hugues de Saxcé , Brian P. Bailey , Chenxi Song , Chenjun Xiao , Dehao Zhang , E Zhang , Fangling Pu , Han Zhu , Jia-Wei Liu , Jonas Bayer , Julien Michel , Longhui Yu , Léo Dreyfus-Schmidt , Lewis Tunstall , Lisa Pagani , Mychal A. Machado , Pauline Bourigault , Ran Wang , Stanislas Polu , Thibaut Barroyer , Weizhong Li , Yazhe Niu , Yann Fleureau , Yangyang Hu , Zhouliang Yu , Zihan Wang , Zhilin Yang , Zhengying Liu , Jia Li ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2504.11354

We introduce Kimina-Prover Preview, a large language model that pioneers a novel reasoning-driven exploration paradigm for formal theorem proving, as showcased in this preview release. Trained with a large-scale reinforcement learning pipeline from Qwen2.5-72B, Kimina-Prover demonstrates strong performance in Lean 4 proof generation by employing a structured reasoning pattern we term \textit{formal reasoning pattern}. This approach allows the model to emulate human problem-solving strategies in Lean, iteratively generating and refining proof steps. Kimina-Prover sets a new state-of-the-art on the miniF2F benchmark, reaching 80.7% with pass@8192. Beyond improved benchmark performance, our work yields several key insights: (1) Kimina-Prover exhibits high sample efficiency, delivering strong results even with minimal sampling (pass@1) and scaling effectively with computational budget, stemming from its unique reasoning pattern and RL training; (2) we demonstrate clear performance scaling with model size, a trend previously unobserved for neural theorem provers in formal mathematics; (3) the learned reasoning style, distinct from traditional search algorithms, shows potential to bridge the gap between formal verification and informal mathematical intuition. We open source distilled versions with 1.5B and 7B parameters of Kimina-Prover

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2504.11354
PDF: https://arxiv.org/pdf/2504.11354
OA Status: green
OpenAlex ID: https://openalex.org/W4416052712

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DOI: https://doi.org/10.48550/arxiv.2504.11354

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Title: Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning

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Language: en

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Publication year: 2025

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Publication date: 2025-04-15

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Authors: Haiming Wang, Mert Unsal, Xiaohan Lin, Mantas Baksys, Junqi Liu, Marco Dos Santos, Flood Sung, Marina Vinyes, Zhenzhe Ying, Ziyue Zhu, Jianqiao Lu, Hugues de Saxcé, Brian P. Bailey, Chenxi Song, Chenjun Xiao, Dehao Zhang, E Zhang, Fangling Pu, Han Zhu, Jia-Wei Liu, Jonas Bayer, Julien Michel, Longhui Yu, Léo Dreyfus-Schmidt, Lewis Tunstall, Lisa Pagani, Mychal A. Machado, Pauline Bourigault, Ran Wang, Stanislas Polu, Thibaut Barroyer, Weizhong Li, Yazhe Niu, Yann Fleureau, Yangyang Hu, Zhouliang Yu, Zihan Wang, Zhilin Yang, Zhengying Liu, Jia Li

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Landing page: https://arxiv.org/abs/2504.11354

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abstract_inverted_index.improved	86
abstract_inverted_index.informal	164
abstract_inverted_index.language	6
abstract_inverted_index.learning	30
abstract_inverted_index.paradigm	14
abstract_inverted_index.pioneers	9
abstract_inverted_index.pipeline	31
abstract_inverted_index.proving,	18
abstract_inverted_index.reaching	81
abstract_inverted_index.refining	69
abstract_inverted_index.release.	24
abstract_inverted_index.sampling	107
abstract_inverted_index.stemming	115
abstract_inverted_index.versions	171
abstract_inverted_index.benchmark	87
abstract_inverted_index.distilled	170
abstract_inverted_index.employing	44
abstract_inverted_index.insights:	94
abstract_inverted_index.introduce	1
abstract_inverted_index.pattern}.	53
abstract_inverted_index.potential	155
abstract_inverted_index.reasoning	47, 52, 119, 147
abstract_inverted_index.showcased	20
abstract_inverted_index.training;	123
abstract_inverted_index.benchmark,	80
abstract_inverted_index.delivering	101
abstract_inverted_index.generating	67
abstract_inverted_index.generation	42
abstract_inverted_index.intuition.	166
abstract_inverted_index.parameters	176
abstract_inverted_index.pass@8192.	84
abstract_inverted_index.previously	135
abstract_inverted_index.strategies	63
abstract_inverted_index.structured	46
abstract_inverted_index.unobserved	136
abstract_inverted_index.algorithms,	153
abstract_inverted_index.demonstrate	126
abstract_inverted_index.effectively	111
abstract_inverted_index.efficiency,	100
abstract_inverted_index.exploration	13
abstract_inverted_index.iteratively	66
abstract_inverted_index.large-scale	28
abstract_inverted_index.performance	37, 128
abstract_inverted_index.traditional	151
abstract_inverted_index.Qwen2.5-72B,	33
abstract_inverted_index.demonstrates	35
abstract_inverted_index.mathematical	165
abstract_inverted_index.mathematics;	143
abstract_inverted_index.performance,	88
abstract_inverted_index.verification	162
abstract_inverted_index.Kimina-Prover	2, 34, 72, 96, 178
abstract_inverted_index.computational	113
abstract_inverted_index.reinforcement	29
abstract_inverted_index.\textit{formal	51
abstract_inverted_index.problem-solving	62
abstract_inverted_index.reasoning-driven	12
abstract_inverted_index.state-of-the-art	76
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	40
citation_normalized_percentile