While Retrieval-Augmented Generation systems enhance Large Language Models by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We presentParetoRAG, an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. By decomposing paragraphs into sentences and dynamically re-weighting core content while preserving contextual coherence, ParetoRAG achieves dual improvements in retrieval precision and generation quality without requiring additional training or API resources, while using only 40% of the tokens compared to traditional RAG approaches. This framework has been empirically validated across various datasets, LLMs, and retrievers. Furthermore, we show that ParetoRAG’s architectural improvements are orthogonally compatible with adaptive noise-robust models, enabling retrieval-augmented optimization and robust training to enhance generation quality mutually. This highlights complementary architectural refinements and noise mitigation, offering insights for integrating retrieval augmentation with robustness enhancement.