推理与个性化结合：释放大型推理模型在个性化生成中的潜力
Reasoning Meets Personalization: Unleashing the Potential of Large Reasoning Model for Personalized Generation
AI总结：
本研究针对大型推理模型在个性化任务中存在的局限，提出强化推理框架及相关机制，经实验验证其性能显著优于现有技术。
摘要：
个性化是现代智能系统中的关键任务，应用范围涵盖与大型语言模型（LLMs）的交互等多个领域。近期推理能力的进展大幅提升了LLMs，使其在数学、编码等任务上实现了前所未有的性能，但它们在个性化任务中的潜力仍未得到充分探索。本文首次对大型推理模型（LRMs）在个性化任务中开展系统性评估，结果令人意外：尽管LRMs生成的token更多，却并未始终优于通用LLMs，尤其在检索密集型场景中，其优势会减弱。我们的分析明确了三大关键局限：思维发散、响应格式不匹配、对检索信息的无效利用。为应对这些挑战，我们提出了用于个性化的强化推理框架（Reinforced Reasoning for Personalization，\nmodel），该框架融入分层推理思维模板，引导LRMs生成结构化输出；此外，我们还提出了推理过程干预方法，以强制其遵守设计的推理模式，提升对齐性；同时提出交叉引用机制以确保一致性。大量实验表明，我们的方法显著优于现有技术。
英文摘要：
Personalization is a critical task in modern intelligent systems, with applications spanning diverse domains, including interactions with large language models (LLMs). Recent advances in reasoning capabilities have significantly enhanced LLMs, enabling unprecedented performance in tasks such as mathematics and coding. However, their potential for personalization tasks remains underexplored. In this paper, we present the first systematic evaluation of large reasoning models (LRMs) for personalization tasks. Surprisingly, despite generating more tokens, LRMs do not consistently outperform general-purpose LLMs, especially in retrieval-intensive scenarios where their advantages diminish. Our analysis identifies three key limitations: divergent thinking, misalignment of response formats, and ineffective use of retrieved information. To address these challenges, we propose Reinforced Reasoning for Personalization (\model), a novel framework that incorporates a hierarchical reasoning thought template to guide LRMs in generating structured outputs. Additionally, we introduce a reasoning process intervention method to enforce adherence to designed reasoning patterns, enhancing alignment. We also propose a cross-referencing mechanism to ensure consistency. Extensive experiments demonstrate that our approach significantly outperforms existing techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
