sandwich rule

**Sandwich Rule** is **supernet training strategy that always samples largest, smallest, and random subnetworks each step.** - It stabilizes one-shot NAS by covering extreme and intermediate model capacities during training. **What Is Sandwich Rule?** - **Definition**: Supernet training strategy that always samples largest, smallest, and random subnetworks each step. - **Core Mechanism**: Min-max subnet sampling regularizes supernet behavior across the full architecture-width spectrum. - **Operational Scope**: It is applied in neural-architecture-search systems to improve robustness, accountability, and long-term performance outcomes. - **Failure Modes**: If random subnet diversity is low, intermediate regions can still be undertrained. **Why Sandwich Rule Matters** - **Outcome Quality**: Better methods improve decision reliability, efficiency, and measurable impact. - **Risk Management**: Structured controls reduce instability, bias loops, and hidden failure modes. - **Operational Efficiency**: Well-calibrated methods lower rework and accelerate learning cycles. - **Strategic Alignment**: Clear metrics connect technical actions to business and sustainability goals. - **Scalable Deployment**: Robust approaches transfer effectively across domains and operating conditions. **How It Is Used in Practice** - **Method Selection**: Choose approaches by uncertainty level, data availability, and performance objectives. - **Calibration**: Adjust random-subnet count and monitor accuracy consistency over sampled size ranges. - **Validation**: Track quality, stability, and objective metrics through recurring controlled evaluations. Sandwich Rule is **a high-impact method for resilient neural-architecture-search execution** - It improves robustness of weight-sharing NAS across deployment budgets.