Retrieval-augmented generation systems often suffer from a gap between optimizing retrieval relevance and generative utility. With such a gap, retrieved documents may be topically relevant but still lack the content needed for effective reasoning during generation. While existing bridge modules attempt to rewrite the retrieved text for better generation, we show how they fail by not capturing "document utility". In this work, we propose R2U, with a key distinction of approximating true utility through joint observation of rewriting and answering in the reasoning process. To distill, R2U scale such supervision to enhance reliability in distillation. We further construct utility-improvement supervision by measuring the generator's gain of the answer under the rewritten context, yielding signals for fine-tuning and preference optimization. We evaluate our method across multiple open-domain question-answering benchmarks. The empirical results demonstrate consistent improvements over strong bridging baselines

翻译：检索增强生成系统常面临检索相关性与生成实用性之间的优化鸿沟。由于这种差距的存在，检索到的文档可能在主题上相关，却仍缺乏生成过程中有效推理所需的内容。尽管现有的桥接模块尝试通过重写检索文本来改善生成效果，但我们指出这些方法因未能捕捉“文档实用性”而存在不足。本研究提出R2U方法，其核心创新在于通过联合观测推理过程中的重写与回答行为来逼近真实实用性。为实现知识蒸馏，R2U扩展此类监督机制以增强蒸馏过程的可靠性。我们进一步构建实用性改进监督信号：通过测量生成器在重写语境下答案质量的提升幅度，为微调和偏好优化提供训练信号。在多个开放域问答基准上的实验表明，该方法相较于现有强基线桥接模型取得了持续性的性能提升。