Leanabell-Prover: Posttraining Scaling in Formal Reasoning
Recent advances in automated theorem proving (ATP) through LLMs have
highlighted the potential of formal reasoning with Lean 4 codes. However, ATP
has not yet be revolutionized by the recent posttraining scaling as
demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the
entire posttraining of ATP, aiming to align it with breakthroughs in reasoning
models in natural languages.To begin, we continual train current ATP models
with a hybrid dataset, which consists of numerous statement-proof pairs, and
additional data aimed at incorporating cognitive behaviors that emulate human
reasoning and hypothesis refinement. Next, we explore reinforcement learning
with the use of outcome reward returned by Lean 4 compiler. Through our
designed continual training and reinforcement learning processes, we have
successfully improved existing formal provers, including both
DeepSeek-Prover-v1.5 and Goedel-Prover, achieving state-of-the-art performance
in the field of whole-proof generation. For example, we achieve a 59.8% pass
rate (pass@32) on MiniF2F. This is an on-going project and we will
progressively update our findings, release our data and training details.
67f664cc902987500a49b197
Jingyuan Zhang
67f664cc902987500a49b198
Qi Wang
67f664cc902987500a49b199
Xingguang Ji
admin_assigned
2025-04-09T16:50:19.951Z
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jxg
user
jixingguang
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Yahui Liu
67f664cc902987500a49b19b
Yang Yue
67f664cc902987500a49b19c
Fuzheng Zhang
admin_assigned
2025-04-09T16:50:50.399Z
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Fuzheng Zhang
user
Edrex
67f664cc902987500a49b19d
Di Zhang
admin_assigned
2025-04-09T16:50:56.751Z
644c8324f02250233d0d67d9
/avatars/feb39d281457c1750f3eada3c060a23e.svg
Di Zhang
user
dizhang
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Guorui Zhou
admin_assigned
2025-04-09T16:51:02.310Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/67c6c570cf87e2d2ebfc81aa/7qAstZtIT86Uwrz3u_anv.jpeg
Guorui Zhou
user
GuoruiZhou
67f664cc902987500a49b19f
Kun Gai
Leanabell-Prover: Posttraining Scaling in Formal Reasoning
Recent advances in automated theorem proving (ATP) through LLMs have
highlighted the potential of formal reasoning with Lean 4 codes. However, ATP
has not yet be revolutionized by the recent posttraining scaling as
demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the
entire posttraining of ATP, aiming to align it with breakthroughs in reasoning
models in natural languages.To begin, we continual train current ATP models
with a hybrid dataset, which consists of numerous statement-proof pairs, and
additional data aimed at incorporating cognitive behaviors that emulate human
reasoning and hypothesis refinement. Next, we explore reinforcement learning
with the use of outcome reward returned by Lean 4 compiler. Through our
designed continual training and reinforcement learning processes, we have
successfully improved existing formal provers, including both
DeepSeek-Prover-v1.5 and Goedel-Prover, achieving state-of-the-art performance
in the field of whole-proof generation. For example, we achieve a 59.8% pass
rate (pass@32) on MiniF2F. This is an on-going project and we will
progressively update our findings, release our data and training details.
Continual training and reinforcement learning of ATP models using Lean 4 improve formal provers and achieve state-of-the-art performance in whole-proof generation.
continual training reinforcement learning Lean 4 provers whole-proof generation MiniF2F pass rate