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[2506.05154] Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement
Summary
The paper presents Knowledgeable-R1, a reinforcement-learning framework designed to enhance retrieval-augmented generation (RAG) by mitigating contextual interference, improving model robustness and reasoning accuracy.
Why It Matters
This research addresses a critical challenge in AI language models where irrelevant or conflicting retrieved information can lead to errors. By proposing a method that balances the use of parametric knowledge with external context, it enhances the reliability of AI systems in knowledge-intensive tasks, which is vital for applications in various fields including natural language processing and information retrieval.
Key Takeaways
- Knowledgeable-R1 improves RAG performance by reducing contextual interference.
- The framework uses a joint sampling scheme for better decision-making in model responses.
- Significant improvements in robustness and reasoning accuracy were observed in experiments.
- The proposed method outperforms state-of-the-art baselines by +22.89% in counterfactual scenarios.
- The approach maintains performance even when retrieved context is fully accurate.
Computer Science > Computation and Language arXiv:2506.05154 (cs) [Submitted on 5 Jun 2025 (v1), last revised 25 Feb 2026 (this version, v4)] Title:Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement Authors:Chenyu Lin, Yilin Wen, Du Su, Hexiang Tan, Fei Sun, Muhan Chen, Chenfu Bao, Zhonghou Lyu View a PDF of the paper titled Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement, by Chenyu Lin and 7 other authors View PDF HTML (experimental) Abstract:Retrieval-augmented generation (RAG) improves performance on knowledge-intensive tasks but can be derailed by wrong, irrelevant, or conflicting retrieved text, causing models to rely on inaccurate evidence and cascade errors. We propose Knowledgeable-R1, a reinforcement-learning framework that explicitly trains large language models to use parametric knowledge (PK) to resist contextual interference while still exploiting external context when it is reliably helpful. Knowledgeable-R1 introduces a joint sampling scheme that generates paired responses with and without retrieval, and learns both local advantages (within each decoding regime) and global advantages under the same input to quantify when to ignore misleading context versus adopt it. We employ an asymmetric advantage transformation that amplifies exploratory behaviors toward parametric knowledge. Experiments show that Knowledgeable-R1 significantly improves robustness and reasoning accuracy in knowledge conflict sce...
