DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for
  Reinforcement Learning and Monte-Carlo Tree Search
We introduce DeepSeek-Prover-V1.5, an open-source language model designed for
theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both
training and inference processes. Pre-trained on DeepSeekMath-Base with
specialization in formal mathematical languages, the model undergoes supervised
fine-tuning using an enhanced formal theorem proving dataset derived from
DeepSeek-Prover-V1. Further refinement is achieved through reinforcement
learning from proof assistant feedback (RLPAF). Beyond the single-pass
whole-proof generation approach of DeepSeek-Prover-V1, we propose RMaxTS, a
variant of Monte-Carlo tree search that employs an intrinsic-reward-driven
exploration strategy to generate diverse proof paths. DeepSeek-Prover-V1.5
demonstrates significant improvements over DeepSeek-Prover-V1, achieving new
state-of-the-art results on the test set of the high school level miniF2F
benchmark (63.5%) and the undergraduate level ProofNet benchmark (25.3%).
66bebba0c55655c7150bb379
Huajian Xin
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2024-08-16T08:34:26.149Z
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HuajianXin
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HuajianXin
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Z. Z. Ren
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Junxiao Song
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2024-08-16T08:34:35.317Z
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Junxiao Song
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haha-point
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Zhihong Shao
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2024-08-16T08:34:41.552Z
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Zhihong Shao
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Wanjia Zhao
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Haocheng Wang
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Bo Liu
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2024-08-16T08:19:20.542Z
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Bo Liu
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Benjamin-eecs
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Liyue Zhang
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Xuan Lu
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2024-08-16T08:35:20.244Z
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Xuan Lu
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lx09
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Qiushi Du
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Wenjun Gao
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2024-08-16T08:35:35.828Z
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wenjun gao
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Qihao Zhu
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2024-08-16T08:35:46.380Z
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zhuqihao
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Dejian Yang
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2024-08-16T08:35:55.805Z
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DejianYang
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DejianYang
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Zhibin Gou
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2024-08-16T08:36:06.134Z
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Zhibin Gou
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zubingou
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Z. F. Wu
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Fuli Luo
admin_assigned
2024-08-16T08:36:54.274Z
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Fuli Luo
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Chong Ruan
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2024-08-17T05:38:46.449Z
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Chong Ruan
user
Chester111
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for
  Reinforcement Learning and Monte-Carlo Tree Search
We introduce DeepSeek-Prover-V1.5, an open-source language model designed for
theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both
training and inference processes. Pre-trained on DeepSeekMath-Base with
specialization in formal mathematical languages, the model undergoes supervised
fine-tuning using an enhanced formal theorem proving dataset derived from
DeepSeek-Prover-V1. Further refinement is achieved through reinforcement
learning from proof assistant feedback (RLPAF). Beyond the single-pass
whole-proof generation approach of DeepSeek-Prover-V1, we propose RMaxTS, a
variant of Monte-Carlo tree search that employs an intrinsic-reward-driven
exploration strategy to generate diverse proof paths. DeepSeek-Prover-V1.5
demonstrates significant improvements over DeepSeek-Prover-V1, achieving new
state-of-the-art results on the test set of the high school level miniF2F
benchmark (63.5%) and the undergraduate level ProofNet benchmark (25.3%).
DeepSeek-Prover-V1.5 improves theorem proving by optimizing training and inference, utilizing reinforcement learning, and proposing RMaxTS for diverse proof paths, achieving state-of-the-art results on miniF2F and ProofNet benchmarks.
DeepSeek-Prover-V1.5 language model theorem proving Lean 4 DeepSeekMath-Base supervised fine-tuning RLPAF Monte-Carlo tree search RMaxTS intrinsic-reward-driven exploration miniF2F ProofNet