Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling

Researchers propose SOCD, an offline reinforcement learning algorithm that learns multi-user scheduling policies from pre-collected data without requiring real-time system interactions. The method combines diffusion models with critic guidance and Lagrangian optimization to handle delay-constrained resource allocation across applications like data centers and live streaming.

This research addresses a fundamental challenge in machine learning deployment: training effective scheduling algorithms without degrading live system performance. Traditional online learning approaches require iterative interactions with production systems, creating operational risk and service quality issues. SOCD circumvents this limitation by learning exclusively from historical datasets, enabling safer, more practical policy development.

The technical contribution centers on combining three elements: diffusion models for policy generation, a critic network for constraint satisfaction, and Lagrangian optimization for handling competing objectives. This architecture allows the system to balance multiple delay constraints across heterogeneous users while respecting resource limitations. The offline-learning paradigm has gained traction across domains where real-time experimentation proves costly or dangerous.

For infrastructure operators and cloud providers, this approach offers immediate practical value. Data center schedulers, messaging platforms, and streaming services constantly grapple with balancing user experience against resource costs. By enabling policy training on historical traffic patterns, SOCD reduces deployment friction and accelerates optimization cycles. The method's demonstrated resilience to partially observable environments and large-scale deployments suggests applicability across diverse operational contexts.

The research indicates a broader industry shift toward offline reinforcement learning in production systems. As organizations accumulate richer datasets, the capacity to extract policy improvements without online risk becomes increasingly valuable. Future work likely explores application-specific variants and integration with existing scheduling frameworks used in major cloud platforms.

• →SOCD enables safe policy learning from historical data, eliminating need for risky online system interactions during training
• →The algorithm combines diffusion models with critic networks and Lagrangian optimization to satisfy multiple delay and resource constraints
• →Method demonstrates robustness across partially observable systems and large-scale deployment scenarios
• →Offline reinforcement learning approach reduces operational costs and deployment friction for infrastructure operators
• →Applicable to data centers, messaging platforms, and streaming services managing diverse user delay sensitivities