Ranking-aware Reinforcement Learning for Ordinal Ranking
This addresses ordinal ranking problems for machine learning applications, representing a novel method for a known bottleneck.
The paper tackles the challenge of modeling ordinal dependencies in ranking tasks by proposing Ranking-Aware Reinforcement Learning (RARL), which integrates regression and Learning-to-Rank with a unified objective and ranking-aware reward, validated on three benchmarks.
Ordinal regression and ranking are challenging due to inherent ordinal dependencies that conventional methods struggle to model. We propose Ranking-Aware Reinforcement Learning (RARL), a novel RL framework that explicitly learns these relationships. At its core, RARL features a unified objective that synergistically integrates regression and Learning-to-Rank (L2R), enabling mutual improvement between the two tasks. This is driven by a ranking-aware verifiable reward that jointly assesses regression precision and ranking accuracy, facilitating direct model updates via policy optimization. To further enhance training, we introduce Response Mutation Operations (RMO), which inject controlled noise to improve exploration and prevent stagnation at saddle points. The effectiveness of RARL is validated through extensive experiments on three distinct benchmarks.