The end-to-end generative paradigm is revolutionizing advertising recommendation systems, driving a shift from traditional cascaded architectures towards unified modeling. However, practical deployment faces three core challenges: the misalignment between interest objectives and business value, the target-agnostic limitation of generative processes, and the disconnection between generation and ranking stages. Existing solutions often fall into a dilemma where single-stage fusion induces optimization tension, while stage decoupling causes irreversible information loss. To address this, we propose OneRanker, achieving architectural-level deep integration of generation and ranking. First, we design a value-aware multi-task decoupling architecture. By leveraging task token sequences and causal mask, we separate interest coverage and value optimization spaces within shared representations, effectively alleviating target conflicts. Second, we construct a coarse-to-fine collaborative target awareness mechanism, utilizing Fake Item Tokens for implicit awareness during generation and a ranking decoder for explicit value alignment at the candidate level. Finally, we propose input-output dual-side consistency guarantees. Through Key/Value pass-through mechanisms and Distribution Consistency (DC) Constraint Loss, we achieve end-to-end collaborative optimization between generation and ranking. The full deployment on Tencent's WeiXin channels advertising system has shown a significant improvement in key business metrics (GMV - Normal +1.34\%), providing a new paradigm with industrial feasibility for generative advertising recommendations.

翻译：端到端生成范式正在革新广告推荐系统，推动着从传统级联架构向统一建模的转变。然而，实际部署面临三个核心挑战：兴趣目标与商业价值之间的错位、生成过程的目标无关性限制，以及生成与排序阶段之间的脱节。现有方案常陷入两难困境：单阶段融合引发优化张力，而阶段解耦则导致不可逆的信息损失。为解决此问题，我们提出了OneRanker，实现了生成与排序在架构层面的深度融合。首先，我们设计了一种价值感知的多任务解耦架构。通过利用任务令牌序列和因果掩码，我们在共享表征中分离了兴趣覆盖与价值优化空间，有效缓解了目标冲突。其次，我们构建了一个由粗到细的协同目标感知机制，在生成阶段利用伪物品令牌进行隐式感知，并在候选级别通过排序解码器实现显式的价值对齐。最后，我们提出了输入-输出双侧一致性保障。通过键/值传递机制和分布一致性约束损失，我们实现了生成与排序之间的端到端协同优化。在腾讯微信频道广告系统中的全面部署显示，关键业务指标（GMV - Normal +1.34%）得到显著提升，为生成式广告推荐提供了一个具备工业可行性的新范式。