Author

Ying Liu

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Jul 2026

SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning

SERTPO (Self-Evolving Rubric Policy Optimization), which replaces answer voting with a closed loop that co-evolves response evidence, query-specific rubrics, and policy parameters, and supports OOD transfer and continued cross-benchmark evolution.

Jian-Ze Wang, Kun-Wang Zheng, Ying Liu et al. · arXiv.org · 2 citations

Preprint Aug 2026

TTPO: Test-Time Policy Optimization

Test-Time Policy Optimization is proposed, an asymmetric objective that distills agreeing rollouts via OPSD and penalizes disagreeing rollouts with Grouped RL and Token-level selection further refines both branches: distillation down-weights already-converged positions, while RL penalizes only confident errors.

Ao-Han Wang, Zhengxi Lu, Jian-Ze Wang et al. · 2 citations