Author

Jing-Li Yang

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#artificial intelligence Mar 2026

P^2O: Joint Policy and Prompt Optimization

Joint Policy and Prompt Optimization (P O) is introduced to mitigate advantage collapse by alternating continuous policy updates with discrete prompt evolution, providing a self-reinforcing framework that facilitates more effective LLM alignment.

Xinyu Lu, Kaiqi Zhang, Jing-Li Yang et al. · arXiv.org · 2 citations

Preprint Aug 2026

Contrastive Branch Policy Optimization

Requiring only outcome rewards and no process-level annotation, CBPO provides a practical solution for fine-grained credit assignment in tool-integrated agent training and consistently outperforms state-of-the-art policy-optimization and branch-based methods.

Ying Wang, Changlin Qiu, Bang Lin et al. · 0 citations