In online advertising, advertising text plays a critical role in attracting user engagement and driving advertiser value. Existing industrial systems typically follow a two-stage paradigm, where candidate texts are first generated and subsequently aligned with online performance metrics such as click-through rate(CTR). This separation often leads to misaligned optimization objectives and low funnel efficiency, limiting global optimality. To address these limitations, we propose RELATE, a reinforcement learning-based end-to-end framework that unifies generation and objective alignment within a single model. Instead of decoupling text generation from downstream metric alignment, RELATE integrates performance and compliance objectives directly into the generation process via policy learning. To better capture ultimate advertiser value beyond click-level signals, We incorporate conversion-oriented metrics into the objective and jointly model them with compliance constraints as multi-dimensional rewards, enabling the model to generate high-quality ad texts that improve conversion performance under policy constraints. Extensive experiments on large-scale industrial datasets demonstrate that RELATE consistently outperforms baselines. Furthermore, online deployment on a production advertising platform yields statistically significant improvements in click-through conversion rate(CTCVR) under strict policy constraints, validating the robustness and real-world effectiveness of the proposed framework.

翻译：在在线广告中，广告文本对于吸引用户参与和提升广告主价值起着至关重要的作用。现有的工业系统通常遵循两阶段范式：首先生成候选文本，随后根据点击率（CTR）等在线性能指标进行对齐。这种分离往往导致优化目标不一致和漏斗效率低下，限制了全局最优性。为应对这些局限，我们提出了RELATE，一种基于强化学习的端到端框架，将生成与目标对齐统一在单一模型中。RELATE不再将文本生成与下游指标对齐解耦，而是通过策略学习，将性能和合规性目标直接整合到生成过程中。为了更好地捕捉超越点击信号的最终广告主价值，我们将面向转化的指标纳入目标，并与合规性约束共同建模为多维奖励，使模型能够在策略约束下生成高质量广告文本，从而提升转化性能。在大规模工业数据集上的广泛实验表明，RELATE持续优于基线方法。此外，在生产广告平台上的在线部署，在严格的策略约束下实现了点击转化率（CTCVR）的统计显著提升，验证了所提框架的鲁棒性和实际有效性。