Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this knowledge for GR remains poorly understood. A key obstacle is that LLM-based GR typically represents items with Semantic IDs (SIDs), disrupting LLMs' natural-language reasoning interface because these tokens are unseen by the LLM during pretraining. Existing approaches address this with expensive multi-stage pipelines that ground SIDs and elicit explicit rationales, but offer limited insight into when and why each stage is necessary. In this work, we systematically decompose explicit reasoning training pipelines for LLM-based GR, revealing three key limitations: weakened world-knowledge verbalization, misalignment between SID and natural-language token embedding spaces, and sensitivity to rationale quality, all of which hurt explicit reasoning performance. To circumvent these issues, we propose PauseRec, a lightweight implicit reasoning paradigm tailored for GR. PauseRec is exceptionally practical, avoiding costly reasoning trace acquisition and reasoning alignment training, leading to a multitude of benefits: (1) it outperforms standard explicit CoT methods by up to 6.22%, (2) it reduces training cost by up to 65% GPU hours, and (3) it speeds up inference by up to 71.3%. These results position PauseRec as a lightweight alternative to explicit rationale generation, enabling more effective and efficient LLM-based GR.

翻译：大语言模型（LLM）越来越多地被用作生成式推荐（GR）的主干，有望利用预训练的世界知识。然而，如何可靠地调用这些知识进行GR仍鲜为人知。一个关键障碍是，基于LLM的GR通常使用语义ID（SID）表示物品，这会破坏LLM的自然语言推理接口，因为这些token在LLM预训练期间是未见过的。现有方法通过昂贵的多阶段流水线来处理这一问题，这些流水线会建立SID的grounding并引发显式推理链条，但对于每个阶段在何时以及为何必要提供了有限的见解。在这项工作中，我们系统地分解了基于LLM的GR的显式推理训练流水线，揭示了三个关键局限性：世界知识语言化的减弱、SID与自然语言token嵌入空间之间的不对齐，以及对推理链条质量的敏感性，所有这些都会损害显式推理性能。为了规避这些问题，我们提出了PauseRec，这是一种专为GR设计的轻量级隐式推理范式。PauseRec非常实用，避免了昂贵的推理链条获取和推理对齐训练，从而带来了众多益处：（1）其性能比标准显式CoT方法高出高达6.22%，（2）训练成本（GPU小时）降低高达65%，（3）推理速度提升高达71.3%。这些结果使PauseRec成为显式推理链条生成的轻量级替代方案，能够实现更有效和高效的基于LLM的GR。