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This research introduces GOAL, a generative framework that optimizes advertising incentives by modeling user fatigue as non-Markovian dynamics within strict global constraints. By formulating allocation as conditional sequence generation with Safe Constrained Policy Optimization (SCPO), the method improves long-term revenue and retention while minimizing ROI violations without retraining for new constraint spectra. The approach advances constrained reinforcement learning in high-frequency, delayed-feedback environments where traditional uplift models struggle to capture complex user behaviors.
Generative Optimization for Incentivized Advertising with Global Level Constraints
arXiv:2608.04421v1 Announce Type: new Abstract: Incentivized advertising allocates monetary or virtual rewards to drive user engagement, where a key challenge is optimizing continuous incentive magnitudes under strict global constraints. This problem is complicated by high-frequency interactions, delayed feedback, and non-Markovian user dynamics such as fatigue, which limit the effectiveness of existing uplift modeling and constrained reinforcement learning approaches. To address these challenges, we propose GOAL, a constraint-aware generative framework that formulates incentive allocation as a conditional sequence generation problem. GOAL directly generates incentive magnitudes conditioned on user histories and system-level global pressure, and integrates a hierarchical causal state encoder to capture both local behavioral dynamics and long-range dependencies. To enable flexible constraint control, we introduce \textbf{S}afe \textbf{C}onstrained \textbf{P}olicy \textbf{O}ptimization (SCPO), which learns a single generative policy that generalizes across a spectrum of ROI constraints without retraining. Experiments on large-scale real-world data and a synthetic fatigue-aware environment show that GOAL improves long-term revenue and user retention while substantially reducing ROI violation rates compared to strong baselines.
Abstract:Incentivized advertising allocates monetary or virtual rewards to drive user engagement, where a key challenge is optimizing continuous incentive magnitudes under strict global constraints. This problem is complicated by high-frequency interactions, delayed feedback, and non-Markovian user dynamics such as fatigue, which limit the effectiveness of existing uplift modeling and constrained reinforcement learning approaches. To address these challenges, we propose GOAL, a constraint-aware generative framework that formulates incentive allocation as a conditional sequence generation problem. GOAL directly generates incentive magnitudes conditioned on user histories and system-level global pressure, and integrates a hierarchical causal state encoder to capture both local behavioral dynamics and long-range dependencies. To enable flexible constraint control, we introduce \textbf{S}afe \textbf{C}onstrained \textbf{P}olicy \textbf{O}ptimization (SCPO), which learns a single generative policy that generalizes across a spectrum of ROI constraints without retraining. Experiments on large-scale real-world data and a synthetic fatigue-aware environment show that GOAL improves long-term revenue and user retention while substantially reducing ROI violation rates compared to strong baselines.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2608.04421 [cs.LG]
(or arXiv:2608.04421v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.04421 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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