LaSER: 将显式推理内化到潜在空间以实现密集检索
LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval
AI总结：
LaSER通过将显式推理内化到潜在空间，提升密集检索性能，结合推理深度与推理效率。
摘要：
大型语言模型（LLMs）已经从根本上改变了密集检索，将回骨从判别编码器升级到生成架构。然而，仍存在关键断层：尽管LLMs具备强大的推理能力，当前检索器主要将其作为静态编码器使用，未能探索其在复杂推理方面的潜力。为了解决这一问题，现有方法通常采用重写后再检索的流程，在检索前生成显式的CoT推理路径。然而，这会带来可接受的延迟。在本文中，我们提出LaSER，一种新的自蒸馏框架，将显式推理内化到密集检索器的潜在空间中。基于共享的LLM回骨，LaSER引入了双视图训练机制：显式视图显式编码真实推理路径，潜视图执行隐式的潜在思考。为了弥合这两个视图之间的差距，我们设计了多粒度对齐策略。除了标准输出对齐外，我们引入了轨迹对齐机制，将潜在路径的中间潜在状态与显式推理段的语义进展同步。这使检索器能够在不进行自回归文本生成的情况下沉默且高效地思考。在域内和域外的推理密集基准测试中，广泛实验表明LaSER显著优于最先进的基线。此外，对不同回骨和模型规模的分析验证了我们方法的鲁棒性，确认了我们的统一学习框架对于激发有效潜在思考的重要性。我们的方法成功地结合了显式CoT流程的推理深度与标准密集检索器的推理效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
