Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for knowledge-intensive tasks, yet its effectiveness in long-context scenarios is often bottlenecked by the retriever's inability to distinguish sparse yet crucial evidence. Standard retrievers, optimized for query-document similarity, frequently fail to align with the downstream goal of generating a precise answer. To bridge this gap, we propose a novel fine-tuning framework that optimizes the retriever for Answer Alignment. Specifically, we first identify high-quality positive chunks by evaluating their sufficiency to generate the correct answer. We then employ a curriculum-based contrastive learning scheme to fine-tune the retriever. This curriculum leverages LLM-constructed Knowledge Graphs (KGs) to generate augmented queries, which in turn mine progressively challenging hard negatives. This process trains the retriever to distinguish the answer-sufficient positive chunks from these nuanced distractors, enhancing its generalization. Extensive experiments on 10 datasets from the Ultradomain and LongBench benchmarks demonstrate that our fine-tuned retriever achieves state-of-the-art performance, improving 14.5\% over the base model without substantial architectural modifications and maintaining strong efficiency for long-context RAG. Our work presents a robust and effective methodology for building truly answer-centric retrievers. Source Code is available on https://github.com/valleysprings/ARK/.

翻译：检索增强生成（RAG）已成为知识密集型任务的一个强大框架，但其在长上下文场景中的有效性常受限于检索器难以区分稀疏却关键的证据。标准的检索器通常针对查询-文档相似性进行优化，往往无法与生成精确答案的下游目标对齐。为弥合这一差距，我们提出了一种新颖的微调框架，旨在优化检索器以实现答案对齐。具体而言，我们首先通过评估文本块是否足以生成正确答案来识别高质量的正样本块。随后，我们采用基于课程的对比学习方案对检索器进行微调。该课程利用大语言模型构建的知识图谱（KG）来生成增强查询，进而挖掘逐步具有挑战性的困难负样本。这一过程训练检索器从这些微妙的干扰项中区分出足以支撑答案的正样本块，从而提升其泛化能力。在Ultradomain和LongBench基准测试的10个数据集上进行的大量实验表明，我们微调后的检索器实现了最先进的性能，在未对架构进行重大修改的情况下，比基础模型提升了14.5%，并在长上下文RAG中保持了强大的效率。我们的工作为构建真正以答案为中心的检索器提供了一种稳健且有效的方法。源代码可在 https://github.com/valleysprings/ARK/ 获取。