Improving DAPO from a Mixed-Policy Perspective

• URL: http://arxiv.org/abs/2507.12931v2
• Date: Fri, 18 Jul 2025 07:37:39 GMT
• Title: Improving DAPO from a Mixed-Policy Perspective
• Authors: Hongze Tan,
• Abstract summary: This paper introduces two novel modifications to the Dynamic sAmpling Policy Optimization (DAPO) algorithm.<n>We first propose a method that incorporates a pre-trained, stable guiding policy to provide off-policy experience.<n>We then extend this idea to re-utilize zero-reward samples, which are often discarded by dynamic sampling strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces two novel modifications to the Dynamic sAmpling Policy Optimization (DAPO) algorithm [1], approached from a mixed-policy perspective. Standard policy gradient methods can suffer from instability and sample inefficiency, particularly in sparse reward settings. To address this, we first propose a method that incorporates a pre-trained, stable guiding policy ($\piphi$) to provide off-policy experience, thereby regularizing the training of the target policy ($\pion$). This approach improves training stability and convergence speed by adaptively adjusting the learning step size. Secondly, we extend this idea to re-utilize zero-reward samples, which are often discarded by dynamic sampling strategies like DAPO's. By treating these samples as a distinct batch guided by the expert policy, we further enhance sample efficiency. We provide a theoretical analysis for both methods, demonstrating that their objective functions converge to the optimal solution within the established theoretical framework of reinforcement learning. The proposed mixed-policy framework effectively balances exploration and exploitation, promising more stable and efficient policy optimization.

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