Author

Wei-Hai Lu

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Jul 2026

Hierarchical Latent Reasoning for LLM-based Recommendation

To further optimize the reasoning trajectory, HiLaR combines final recommendation feedback with layer-aware process rewards derived from the marginal target-likelihood gain of each state, and generally outperforms strong sequential, generative, and LLM-based recommendation baselines.

Peiyu Hu, Siying Gu, Weihai Lu et al. · arXiv.org · 1 citation

Book Open access Jul 2026

Think, But Don't Tell: Implicit Reasoning for LLM-based Sequential Recommendation via Multi-Teacher Distillation

I Reasoning via Multi-Teacher Distillation is proposed, a novel framework that 'compiles' the reasoning abilities of large teacher LLMs into a lightweight student Small Language Model (SLM), which significantly outperforms state-of-the-art baselines in both recommendation accuracy and inference efficiency.

Weihai Lu, Xiaoxi Cui, Chenke Yin · Annual International ACM SIG... · 0 citations