Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content. Most existing methods treat SIDs as predefined and train recommenders under static indexing. In practice, SIDs are typically optimized only for content reconstruction rather than recommendation accuracy. This leads to an objective mismatch: the system optimizes an indexing loss to learn the SID and a recommendation loss for interaction prediction, but because the tokenizer is trained independently, the recommendation loss cannot update it. A natural approach is to make semantic indexing differentiable so that recommendation gradients can directly influence SID learning, but this often causes codebook collapse, where only a few codes are used. We attribute this issue to early deterministic assignments that limit codebook exploration, resulting in imbalance and unstable optimization. In this paper, we propose DIGER (Differentiable Semantic ID for Generative Recommendation), a first step toward effective differentiable semantic IDs for generative recommendation. DIGER introduces Gumbel noise to explicitly encourage early-stage exploration over codes, mitigating codebook collapse and improving code utilization. To balance exploration and convergence, we further design two uncertainty decay strategies that gradually reduce the Gumbel noise, enabling a smooth transition from early exploration to exploitation of learned SIDs. Extensive experiments on multiple public datasets demonstrate consistent improvements from differentiable semantic IDs. These results confirm the effectiveness of aligning indexing and recommendation objectives through differentiable SIDs and highlight differentiable semantic indexing as a promising research direction. Our code is released under https://github.com/junchen-fu/DIGER.

翻译：生成式推荐提供了一种新颖范式，其中每个项目由从丰富内容中学习到的离散语义ID（SID）表示。现有方法大多将SID视为预定义的，并在静态索引下训练推荐器。在实践中，SID通常仅针对内容重建而非推荐准确性进行优化，这导致目标不匹配：系统通过索引损失学习SID，并通过交互预测的推荐损失进行优化，但由于分词器独立训练，推荐损失无法更新它。一个自然的思路是使语义索引可微分，使推荐梯度能直接影响SID学习，但这常引发码本坍塌问题，即仅少数代码被使用。我们将此归因于早期确定性分配限制了码本探索，导致不平衡与不稳定优化。本文提出了DIGER（可微分语义ID用于生成式推荐），首次实现了面向生成式推荐的有效可微分语义ID。DIGER引入Gumbel噪声显式鼓励代码的早期探索，缓解码本坍塌并提高代码利用率。为平衡探索与收敛，我们进一步设计了两种不确定性衰减策略，逐步减少Gumbel噪声，实现从早期探索到已学习SID利用的平滑过渡。在多个公开数据集上的大量实验表明，可微分语义ID带来了持续改进。这些结果验证了通过可微分SID对齐索引与推荐目标的有效性，并凸显了可微分语义索引作为有前景的研究方向。我们的代码已发布于https://github.com/junchen-fu/DIGER。