長程決策中的成對偏好強化學習：Markov Decision Contest 的機制與邊界
長程決策中，標量獎勵難以精確定義，而成對偏好方法在理論保證上存在缺口。研究提出的 Markov Decision Contest 模型將求解複雜度證明為 P 類，並提出 HPI 與 HPI-Clip 算法。實測顯示近似算法在多個高維任務中學習效率優於 SPPO，為長程成對偏好決策提供新的邊界與取捨。
Optimization
Pairwise Preference Reinforcement Learning in Long-Horizon Decisions: The Mechanisms and Boundaries of Markov Decision Contest
In long-horizon decision-making, scalar rewards are difficult to define precisely, while pairwise preference methods have gaps in their theoretical guarantees. Markov Decision Contest proves that its exact solution is in P and introduces the HPI and HPI-Clip algorithms. Experiments across seven high-dimensional control tasks show that the approximate HPI-Clip method learns more efficiently than SPPO. The result defines a new set of boundaries and trade-offs for long-horizon decisions based on pairwise preferences.