Generative Sequential Notification Optimization via Multi-Objective Decision Transformers

• URL: http://arxiv.org/abs/2509.02458v1
• Date: Tue, 02 Sep 2025 16:09:02 GMT
• Title: Generative Sequential Notification Optimization via Multi-Objective Decision Transformers
• Authors: Borja Ocejo, Ruofan Wang, Ke Liu, Rohit K. Patra, Haotian Shen, David Liu, Yiwen Yuan, Gokulraj Mohanasundaram, Fedor Borisyuk, Prakruthi Prabhakar,
• Abstract summary: We present a Decision Transformer based framework that reframes policy learning as return-conditioned supervised learning.<n>Our contributions include a real-world comparison with CQL, a multi-reward design suitable for non-episodic tasks, and a quantile regression approach to return-to-go conditioning.
• Score: 9.542285455613927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Notifications are an important communication channel for delivering timely and relevant information. Optimizing their delivery involves addressing complex sequential decision-making challenges under constraints such as message utility and user fatigue. Offline reinforcement learning (RL) methods, such as Conservative Q-Learning (CQL), have been applied to this problem but face practical challenges at scale, including instability, sensitivity to distribution shifts, limited reproducibility, and difficulties with explainability in high-dimensional recommendation settings. We present a Decision Transformer (DT) based framework that reframes policy learning as return-conditioned supervised learning, improving robustness, scalability, and modeling flexibility. Our contributions include a real-world comparison with CQL, a multi-reward design suitable for non-episodic tasks, a quantile regression approach to return-to-go conditioning, and a production-ready system with circular buffer-based sequence processing for near-real-time inference. Extensive offline and online experiments in a deployed notification system show that our approach improves notification utility and overall session activity while minimizing user fatigue. Compared to a multi-objective CQL-based agent, the DT-based approach achieved a +0.72% increase in sessions for notification decision-making at LinkedIn by making notification recommendation more relevant.

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