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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20282 |
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| _version_ | 1866913998995193856 |
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| author | Gao, Zitian Chen, Lynx Luo, Haoming Zhou, Joey Dai, Bryan |
| author_facet | Gao, Zitian Chen, Lynx Luo, Haoming Zhou, Joey Dai, Bryan |
| contents | We trained 13,440 large language models and found that entropy minimization requires only a single unlabeled data and 10 steps optimization to achieve performance improvements comparable to or even greater than those obtained using thousands of data and carefully designed rewards in rule-based reinforcement learning. This striking result may prompt a rethinking of post-training paradigms for large language models. Our code is avaliable at https://github.com/zitian-gao/one-shot-em. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One-shot Entropy Minimization Gao, Zitian Chen, Lynx Luo, Haoming Zhou, Joey Dai, Bryan Computation and Language We trained 13,440 large language models and found that entropy minimization requires only a single unlabeled data and 10 steps optimization to achieve performance improvements comparable to or even greater than those obtained using thousands of data and carefully designed rewards in rule-based reinforcement learning. This striking result may prompt a rethinking of post-training paradigms for large language models. Our code is avaliable at https://github.com/zitian-gao/one-shot-em. |
| title | One-shot Entropy Minimization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.20282 |