Summary of Deepseek-prover-v1.5: Harnessing Proof Assistant Feedback For Reinforcement Learning and Monte-carlo Tree Search, by Huajian Xin et al.
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
by Huajian Xin, Z.Z. Ren, Junxiao Song, Zhihong Shao, Wanjia Zhao, Haocheng Wang, Bo Liu, Liyue Zhang, Xuan Lu, Qiushi Du, Wenjun Gao, Qihao Zhu, Dejian Yang, Zhibin Gou, Z.F. Wu, Fuli Luo, Chong Ruan
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents an open-source language model called DeepSeek-Prover-V1.5, designed for theorem proving in Lean 4. This improved version enhances training and inference processes compared to its predecessor, DeepSeek-Prover-V1. The model is pre-trained on a formal mathematical language dataset and fine-tuned using a theorem proving dataset with reinforcement learning from proof assistant feedback (RLPAF). Additionally, the paper proposes RMaxTS, a Monte-Carlo tree search variant that employs an intrinsic-reward-driven exploration strategy to generate diverse proof paths. DeepSeek-Prover-V1.5 achieves state-of-the-art results on miniF2F and ProofNet benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DeepSeek-Prover-V1.5 is a new tool for helping computers prove math theorems. It’s designed to work with Lean 4, a specific way of writing math proofs. The tool gets better at its job by learning from lots of examples and getting feedback on its proof-writing skills. It also tries out different ways of proving things to find the best path. This new tool does even better than the old one at solving math problems, which could be useful for education or research. |
Keywords
» Artificial intelligence » Inference » Language model » Reinforcement learning
