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WhisperRec introduces a novel latent reasoning framework for foundation recommendation models (FRMs) that addresses the inefficiencies of traditional Chain-of-Thought (CoT) approaches. By compressing teacher-generated CoT into learnable latent reasoning tokens, it enables reasoning in latent space without the overhead of verbose rationales, thereby maintaining decision-relevant information while significantly enhancing inference speed. Experimental results on large-scale datasets demonstrate that WhisperRec outperforms both explicit-CoT methods and conventional baselines, achieving a 17.44% improvement in recommendation accuracy and over 10x higher online inference throughput.
Latent reasoning can boost recommendation efficiency by over 10x while enhancing accuracy, challenging the need for verbose rationales in LLM applications.
Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs). Existing approaches typically enhance recommendation with explicit Chain-of-Thought (CoT) under the Think-then-Answer paradigm. However, generating lengthy rationales introduces substantial inference overhead, while fixed CoT templates struggle to model diverse, dynamic, and context-dependent user interests. We propose WhisperRec, an efficient latent reasoning framework for FRMs. WhisperRec compresses teacher-generated CoT into learnable latent reasoning tokens, enabling a Latent-Reason-then-Answer paradigm that performs reasoning in latent space without producing verbose rationales. This design retains decision-relevant reasoning information while avoiding the latency bottleneck of autoregressive rationale generation. Specifically, it first introduces Multi-View Adaptive CoT (MV-ACoT) to construct diverse, high-quality supervision from complementary perspectives on user interests. MV-ACoT also adapts reasoning complexity to each instance, applying lightweight analysis to clear cases and targeted multi-factor reasoning to challenging ones. Building on a pre-trained FRM, WhisperRec then employs a three-stage Latent Reasoning Alignment procedure to progressively internalize teacher CoT into latent representations. Finally, curriculum-based post-training activates latent-token reasoning for downstream recommendation while preserving standard recommendation capability. Experiments on an industrial-scale Kuaishou dataset and the public Kuaishou LLM-Rec benchmark show that WhisperRec consistently outperforms explicit-CoT methods and conventional baselines. Compared with explicit CoT Think and No-Think variants, WhisperRec improves SID@64 by 17.44% and 9.33%, respectively, and achieves over 10x higher online inference throughput.
