While dense retrieval models have become the standard for state-of-the-art information retrieval, their deployment is often constrained by high memory requirements and reliance on GPU accelerators for vector similarity search. Learned sparse retrieval offers a compelling alternative by enabling efficient search via inverted indices, yet it has historically received less attention than dense approaches. In this report, we introduce LACONIC, a family of learned sparse retrievers based on the Llama-3 architecture (1B, 3B, and 8B). We propose a streamlined two-phase training curriculum consisting of (1) weakly supervised pre-finetuning to adapt causal LLMs for bidirectional contextualization and (2) high-signal finetuning using curated hard negatives. Our results demonstrate that LACONIC effectively bridges the performance gap with dense models: the 8B variant achieves a state-of-the-art 60.2 nDCG on the MTEB Retrieval benchmark, ranking 15th on the leaderboard as of January 1, 2026, while utilizing 71\% less index memory than an equivalent dense model. By delivering high retrieval effectiveness on commodity CPU hardware with a fraction of the compute budget required by competing models, LACONIC provides a scalable and efficient solution for real-world search applications.

翻译：尽管密集检索模型已成为最先进信息检索的标准，但其部署往往受限于高内存需求以及对向量相似性搜索的GPU加速器的依赖。学习型稀疏检索通过倒排索引实现高效搜索，提供了一个引人注目的替代方案，但历史上其受关注程度一直低于密集检索方法。本报告介绍了LACONIC——一个基于Llama-3架构（1B、3B和8B）的学习型稀疏检索模型系列。我们提出了一种简化的两阶段训练课程，包括：（1）弱监督预微调，使因果LLM适应双向上下文建模；（2）使用精选困难负样本进行高信号微调。我们的结果表明，LACONIC有效弥合了与密集模型的性能差距：其8B变体在MTEB检索基准测试中达到了最先进的60.2 nDCG，截至2026年1月1日在排行榜上位列第15名，同时比同等密集模型减少了71%的索引内存。通过在商用CPU硬件上以远低于竞争模型所需的计算预算实现高检索效能，LACONIC为现实世界的搜索应用提供了一个可扩展且高效的解决方案。