GPG: Generalized Policy Gradient Theorem for Transformer-based Policies
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915668469743616 |
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| author | Mao, Hangyu Dong, Guangting Dou, Zhicheng |
| author_facet | Mao, Hangyu Dong, Guangting Dou, Zhicheng |
| contents | We present the Generalized Policy Gradient (GPG) Theorem, specifically designed for Transformer-based policies. Notably, we demonstrate that both standard Policy Gradient Theorem and GRPO emerge as special cases within our GPG framework. Furthermore, we explore its practical applications in training Large Language Models (LLMs), offering new insights into efficient policy optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPG: Generalized Policy Gradient Theorem for Transformer-based Policies Mao, Hangyu Dong, Guangting Dou, Zhicheng Machine Learning Artificial Intelligence Computation and Language We present the Generalized Policy Gradient (GPG) Theorem, specifically designed for Transformer-based policies. Notably, we demonstrate that both standard Policy Gradient Theorem and GRPO emerge as special cases within our GPG framework. Furthermore, we explore its practical applications in training Large Language Models (LLMs), offering new insights into efficient policy optimization. |
| title | GPG: Generalized Policy Gradient Theorem for Transformer-based Policies |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2512.10365 |