Generative Optimization for Incentivized Advertising with Global Level Constraints

• URL: http://arxiv.org/abs/2608.04421v1
• Date: Wed, 05 Aug 2026 04:04:07 GMT
• Title: Generative Optimization for Incentivized Advertising with Global Level Constraints
• Abstract summary: GOAL is a constraint-aware generative framework that formulates incentive allocation as a conditional sequence generation problem.<n>We show that GOAL improves long-term revenue and user retention while substantially reducing ROI violation rates compared to strong baselines.
• Score: 22.46351828626369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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