GPG: Generalized Policy Gradient Theorem for Transformer-based Policies
By: Hangyu Mao, Guangting Dong, Zhicheng Dou
Potential Business Impact:
Teaches AI to learn better and faster.
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.
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