RAPS-DA

RAPS-DA: Regime-aware peer specialization for robust RAG under knowledge conflicts

A new arXiv preprint introduces RAPS-DA, a training framework for making RAG systems more robust when retrieved context conflicts with a model's parametric knowledge. The approach divides conflicts into three reliability regimes (Grounding, Arbitration, Resistance) and trains separate peer specialist models per regime from a shared base, using reverse-KL supervision and a dual-layer token selector to filter uninformative training signals. Peer specialists exist only during training, so the deployed student model requires no additional components at inference time. Experiments across five conflict scenarios and two out-of-distribution benchmarks show RAPS-DA outperforms prompting, decoding, fine-tuning, RL, and single-teacher baselines.