Large language models (LLMs) have recently been adopted for recommendation by framing user preference modeling as a language generation problem. However, existing latent reasoning approaches typically represent user intent with a single latent vector, which struggles to capture the inherently multi-faceted nature of user preferences. We propose Factorized Latent Reasoning (FLR), a novel framework for LLM-based sequential recommendation that decomposes latent reasoning into multiple disentangled preference factors. FLR introduces a lightweight multi-factor attention module that iteratively refines a latent thought representation, where each factor attends to distinct aspects of the user's interaction history. To encourage diversity and specialization, we design orthogonality, attention diversity, and sparsity regularization objectives, and dynamically aggregate factor contributions for the final prediction. We further integrate FLR with an efficient reinforcement learning strategy based on group-relative policy optimization, enabling stable alignment directly in the latent reasoning space. Experiments on multiple benchmarks show that FLR consistently outperforms strong baselines while improving robustness and interpretability.

翻译：大语言模型近期通过将用户偏好建模重构为语言生成问题而被应用于推荐系统。然而，现有潜在推理方法通常采用单一潜在向量表征用户意图，难以捕捉用户偏好固有的多面性特征。我们提出分式潜在推理框架——一种面向基于LLM的序列推荐的新型框架，该框架将潜在推理解耦为多个可分离的偏好因子。FLR引入轻量级多头注意力模块，通过迭代优化潜在思维表征，使每个因子聚焦于用户交互历史的不同维度。为促进多样性与专门化，我们设计了正交性、注意力多样性与稀疏性正则化目标，并动态聚合各因子的贡献以完成最终预测。我们进一步将FLR与基于群组相对策略优化的高效强化学习策略相融合，实现在潜在推理空间中的稳定对齐。多基准实验表明，FLR在提升鲁棒性与可解释性的同时，持续超越强基线方法。