SRPO: Structure-aware Relative Policy Optimization for Ranking
arXiv:2607.25268v1 introduces SRPO, a Structure-aware Relative Policy Optimization framework for listwise ranking. Existing reinforcement learning-based ranking methods treat each sampled permutation as an atomic output, evaluated primarily through a scalar reward, which overlooks structural relationships among different ranking lists. This can lead to inaccurate credit assignment and overly aggressive policy updates. SRPO addresses this by measuring the discrepancy between sampled permutations using a top-weighted Kendall distance, enabling more nuanced optimization signals. The framework aims to improve ranking performance in information access systems by incorporating structural awareness into policy optimization.
Key facts
- arXiv:2607.25268v1 introduces SRPO framework
- SRPO stands for Structure-aware Relative Policy Optimization
- Existing RL ranking methods treat permutations as atomic outputs
- Current methods use scalar rewards, ignoring structural relationships
- This can cause inaccurate credit assignment and aggressive policy updates
- SRPO measures permutation discrepancy via top-weighted Kendall distance
- The framework is designed for listwise ranking tasks
- It aims to improve ranking in information access systems
Entities
Institutions
- arXiv