Reinforced Large Language Model is a formal theorem prover
Fuente:
arXiv
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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913689196560384 |
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| author | Luo, Zhiling |
| author_facet | Luo, Zhiling |
| contents | To take advantage of Large Language Model in theorem formalization and proof, we propose a reinforcement learning framework to iteratively optimize the pretrained LLM by rolling out next tactics and comparing them with the expected ones. The experiment results show that it helps to achieve a higher accuracy compared with directly fine-tuned LLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08908 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforced Large Language Model is a formal theorem prover Luo, Zhiling Artificial Intelligence To take advantage of Large Language Model in theorem formalization and proof, we propose a reinforcement learning framework to iteratively optimize the pretrained LLM by rolling out next tactics and comparing them with the expected ones. The experiment results show that it helps to achieve a higher accuracy compared with directly fine-tuned LLM. |
| title | Reinforced Large Language Model is a formal theorem prover |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.08908 |