Onelatent boosts recommendation speed and accuracy using latent reasoning
OneLatent: Latent Reasoning for Efficient Foundation Recommendation Models
Information Retrieval
Summary
Recommender systems often use large language models that think step-by-step to improve suggestions, but this takes a lot of time and effort. The authors designed OneLatent, which shrinks these step-by-step thought processes into a few internal tokens, making recommendations faster and still accurate. They created a new way to teach the model to adapt reasoning to each user's interests and progressively learn these shortcuts. Tests on a big real-world dataset showed OneLatent gives better recommendations and is much faster than existing methods. When put into production for online ads, it increased revenue by almost 10%.
What this means in practice
- •For advertising platform engineers: Deploy systems that generate faster, more accurate real-time ad recommendations by internalizing complex reasoning into efficient latent tokens.$Commercial implications: This enables ad platforms to sell improved recommendation services that increase revenue with less compute cost.
- •For e-commerce platform developers: Implement quick, reasoning-powered recommendation models to enhance product suggestions without expensive latency from verbose reasoning outputs.
Authors
Hao Jiang, Peiru Du, Pengfei Yao, Mengting Li, Siyuan Lou, Kuo Cai, Sheng Yu, Qiang Luo, Jian Liang, Ruiming Tang, Fei Pan, Peng Jiang, Wenwu Ou
Abstract
Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their use as the backbone of foundation recommendation models (FRMs). Existing methods enhance recommendations through explicit Chain-of-Thought (CoT) reasoning under a Think-then-Answer paradigm. However, explicit CoT incurs substantial inference overhead by generating lengthy reasoning traces and relies on manually designed templates that struggle to capture diverse, dynamic user interests. We propose OneLatent, an efficient latent reasoning framework that compresses explicit reasoning traces into several learnable latent tokens, enabling Latent-Reason-then-Answer inference without generating verbose traces. OneLatent first introduces Multi-View Adaptive CoT (MV-ACoT), which creates diverse, high-quality teacher-generated supervision by exploring user interests from multiple perspectives and automatically adapting reasoning complexity to each instance. Building on pretrained FRMs, it then uses a three-stage latent-token alignment paradigm to progressively internalize CoT traces into learnable latent tokens. Finally, a multistage curriculum-based post-training strategy activates latent-token reasoning for downstream recommendation tasks. Experiments on an industrial-scale Kuaishou dataset and the public Kuaishou LLM-Rec benchmark show that OneLatent consistently outperforms explicit CoT-based methods and traditional baselines. Compared with the Think and No-Think variants of FRMs, OneLatent improves SID@64 by 17.44% and 9.33%, respectively, while achieving over 17x higher online inference throughput. We further develop a production serving system for scalable, real-time FRM inference. An online A/B test in Kuaishou's local-services advertising scenario shows that deploying OneLatent with this system yields an estimated 9.6% revenue lift over strong online baselines, including OneRec and OneReason.