提出统一框架KMR，用三要素系统化管理模型压缩技术。
Formal Algorithms for Model Efficiency
- 将剪枝、量化等技术抽象为可调节旋钮、规则和度量表
- 支持多技术组合优化，实现预算约束下的自动效率提升
- 适合研究模型压缩与自动化机器学习的学者使用
我们提出Knob-Meter-Rule（KMR）框架，一种用于表示和推理深度学习模型效率技术的统一形式化方法。通过将剪枝、量化、知识蒸馏和参数高效架构等多种方法抽象为一致的可控旋钮、确定性规则和可测量指标，KMR提供了数学精确且模块化的效率优化视角。该框架支持多种技术的系统性组合、灵活策略驱动的应用以及通过预算式KMR算法实现的迭代优化。我们展示了如何将知名效率方法实例化为KMR三元组，并为每种方法提供简洁的算法模板。框架揭示了方法间的内在关联，促进混合流水线设计，为未来自动化策略学习、动态适应和成本-质量权衡的理论分析奠定基础。总体而言，KMR既提供概念统一，也具备实际应用价值，推动模型效率研究的发展。
原文摘要 · Abstract (English)
We introduce the Knob-Meter-Rule (KMR) framework, a unified formalism for representing and reasoning about model efficiency techniques in deep learning. By abstracting diverse methods, including pruning, quantization, knowledge distillation, and parameter-efficient architectures, into a consistent set of controllable knobs, deterministic rules, and measurable meters, KMR provides a mathematically precise and modular perspective on efficiency optimization. The framework enables systematic composition of multiple techniques, flexible policy-driven application, and iterative budgeted optimization through the Budgeted-KMR algorithm. We demonstrate how well-known efficiency methods can be instantiated as KMR triples and present concise algorithmic templates for each. The framework highlights underlying relationships between methods, facilitates hybrid pipelines, and lays the foundation for future research in automated policy learning, dynamic adaptation, and theoretical analysis of cost-quality trade-offs. Overall, KMR offers both a conceptual and practical tool for unifying and advancing model efficiency research.
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