Structure-aware Relative Policy Optimization for Ranking
Ranking is a fundamental component of modern information access systems. Reinforcement learning (RL) provides a flexible framework for directly optimizing coarse-grained feedback and system-level objectives defined over the complete ranking list. However, existing RL-based ranking methods typically treat each sampled permutation as an atomic output and evaluate it primarily through a scalar reward, overlooking the structural relationships among different ranking lists. Consequently, permutations with similar rewards but substantially different permutation patterns may receive comparable optimization signals, potentially leading to inaccurate credit assignment and overly aggressive policy ...
| Authors | Yiteng Tu, Weihang Su, Zitao Su et al. |
|---|---|
| Primary category | cs.IR |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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