Generative Recommendation has emerged as a transformative paradigm, reformulating recommendation as an end-to-end autoregressive sequence generation task. Despite its promise, existing preference optimization methods typically rely on binary outcome correctness, suffering from a systemic limitation we term uncertainty blindness. This issue manifests in the neglect of the model's intrinsic generation confidence, the variation in sample learning difficulty, and the lack of explicit confidence expression, directly leading to unstable training dynamics and unquantifiable decision risks. In this paper, we propose Uncertainty-aware Generative Recommendation (UGR), a unified framework that leverages uncertainty as a critical signal for adaptive optimization. UGR synergizes three mechanisms: (1) an uncertainty-weighted reward to penalize confident errors; (2) difficulty-aware optimization dynamics to prevent premature convergence; and (3) explicit confidence alignment to empower the model with confidence expression capabilities. Extensive experiments demonstrate that UGR not only yields superior recommendation performance but also fundamentally stabilizes training, preventing the performance degradation often observed in standard methods. Furthermore, the learned confidence enables reliable downstream risk-aware applications. Our project repository is available at: https://github.com/cxfann/UGR.

翻译：生成式推荐已成为一种变革性范式，将推荐任务重构为端到端的自回归序列生成。尽管前景广阔，但现有的偏好优化方法通常依赖于二值结果的正确性，这导致了一种系统性缺陷，我们称之为不确定性盲视。该问题表现为模型忽略了内在的生成置信度、样本学习难度的差异性以及缺乏显式的置信度表达能力，直接引发了不稳定的训练动态和不可量化的决策风险。在本文中，我们提出不确定性感知的生成式推荐（UGR），这是一个利用不确定性作为关键信号进行自适应优化的统一框架。UGR协同了三种机制：（1）不确定性加权奖励，用于惩罚自信的错误预测；（2）难度感知的优化动态，防止过早收敛；（3）显式置信度对齐，赋予模型置信度表达能力。大量实验表明，UGR不仅取得了更优的推荐性能，还从根本上稳定了训练过程，避免了标准方法中常见的性能退化问题。此外，学习到的置信度能够支持可靠的下游风险感知应用。我们的项目仓库地址为：https://github.com/cxfann/UGR。