动态调整检索深度，让推理模型又快又准。
Cost-Aware Retrieval-Augmentation Reasoning Models with Adaptive Retrieval Depth
- 根据查询和检索结果自适应调节文档列表长度。
- 延迟降低16%-20%，准确率平均提升5%（精确匹配）。
- 适合对效率与性能平衡有要求的问答系统应用。
推理模型因出色表现受到广泛关注，尤其在引入检索增强后。然而，此类模型常伴随高昂的计算开销，因检索与推理阶段均消耗大量资源。本文提出一种检索增强型推理模型，可基于查询及检索结果动态调整检索文档列表长度；设计了一种面向成本的强化学习优势函数，用于训练高效检索增强推理模型；并探索了内存与延迟受限场景下，该框架在近端与分组相对策略优化算法中的实现。在七个公开问答数据集上评估表明，该方法显著提升效率，且不牺牲有效性：模型延迟平均下降16%-20%，精确匹配得分平均提升约5%。
原文摘要 · Abstract (English)
Reasoning models have gained significant attention due to their strong performance, particularly when enhanced with retrieval augmentation. However, these models often incur high computational costs, as both retrieval and reasoning tokens contribute substantially to the overall resource usage. In this work, we make the following contributions: (1) we propose a retrieval-augmented reasoning model that dynamically adjusts the length of the retrieved document list based on the query and retrieval results; (2) we develop a cost-aware advantage function for training of efficient retrieval-augmented reasoning models through reinforcement learning; and (3) we explore both memory- and latency-bound implementations of the proposed cost-aware framework for both proximal and group relative policy optimization algorithms. We evaluate our approach on seven public question answering datasets and demonstrate significant efficiency gains, without compromising effectiveness. In fact, we observed that the model latency decreases by ~16-20% across datasets, while its effectiveness increases by ~5% on average, in terms of exact match.
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