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 finetuning 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 curriculumbased contrastive learning scheme to fine-tune the retriever.This curriculum leverages LLMconstructed 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.