Abstract
LLMs have fundamentally transformed dense retrieval, upgrading backbones from discriminative encoders to generative architectures. However, a critical disconnect remains: while LLMs possess strong reasoning capabilities, current retrievers predominantly utilize them as static encoders, leaving their potential for complex reasoning unexplored. To address this, existing approaches typically adopt rewrite-then-retrieve pipelines to generate explicit CoT rationales before retrieval. However, this incurs prohibitive latency. In this paper, we propose LaSER, a novel self-distillation framework that internalizes explicit reasoning into the latent space of dense retrievers. Operating on a shared LLM backbone, LaSER introduces a dual-view training mechanism: an Explicit view that explicitly encodes ground-truth reasoning paths, and a Latent view that performs implicit latent thinking. To bridge the gap between these views, we design a multi-grained alignment strategy. Beyond standard output alignment, we introduce a trajectory alignment mechanism that synchronizes the intermediate latent states of the latent path with the semantic progression of the explicit reasoning segments. This allows the retriever to think silently and effectively without autoregressive text generation. Extensive experiments on both in-domain and out-of-domain reasoning-intensive benchmarks demonstrate that LaSER significantly outperforms state-of-the-art baselines. Furthermore, analyses across diverse backbones and model scales validate the robustness of our approach, confirming that our unified learning framework is essential for eliciting effective latent thinking. Our method successfully combines the reasoning depth of explicit CoT pipelines with the inference efficiency of standard dense retrievers.
Abstract (translated)
大型语言模型（LLMs）从根本上改变了密集检索，将骨干从判别式编码器升级为生成性架构。然而，一个关键的脱节依然存在：尽管LLMs具备强大的推理能力，当前的检索器主要将其用作静态编码器，未充分利用其进行复杂推理的能力。为了应对这一问题，现有方法通常采用重写后再检索管道，在检索之前生成明确的原因链条（CoT）。然而，这会导致不可接受的时间延迟。在本文中，我们提出了LaSER，一种新的自我蒸馏框架，将显式的推理内部化到密集检索器的潜在空间中。基于共享的大规模语言模型骨干结构，LaSER引入了双视图训练机制：明确视图（Explicit view），该视图显式编码真实的推理路径；以及隐含视图（Latent view），该视图执行隐性的潜在思考。为了弥合这两种视角之间的差距，我们设计了一种多粒度对齐策略。除标准输出对齐外，我们还引入了轨迹对齐机制，以同步隐性路径中的中间潜在状态与显式推理片段的语义进展。这使得检索器能够在不进行自回归文本生成的情况下有效地进行无声思考。 在行业内和行业外密集推理基准上的广泛实验表明，LaSER显著优于现有的最先进基线方法。此外，在不同骨干结构和模型规模下的分析验证了我们方法的强大性能，确认统一学习框架对于提取有效的潜在思维至关重要。我们的方法成功地将显式CoT管道的推理深度与标准密集检索器的推断效率相结合。
URL
https://arxiv.org/abs/2603.01425
