用廉价不准确标签训练优化模型，显著降低计算成本。
Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels
- 先用廉价标签预训练，再自监督微调，三阶段提升效率
- 在多个复杂场景中实现59倍计算成本降低，精度与可行性更好
- 只需少量不精确标签即可启动，适合资源受限的优化任务
为提升优化与仿真的可扩展性，现有方法常训练机器学习代理模型，在推理时以低成本映射问题参数到解。但常见方法依赖昂贵高质量标签或面临复杂优化景观。为此，本文提出新框架：收集廉价不完美标签，通过基于效益损失的终止策略进行监督预训练，最终以自监督学习精细模型。在非凸约束优化、电网运行和刚性动力系统等挑战性领域验证表明，该三阶段策略实现更快收敛、更高精度、可行性和最优性，并带来最高达59倍的离线计算成本削减。进一步分析发现：(i) 效益损失是有效信号；(ii) 少量廉价不精确标签即可将模型置于利于自监督学习的有利状态。
原文摘要 · Abstract (English)
To scale optimization and simulation, prior work has explored training machine-learning surrogates that map problem parameters to solutions inexpensively at inference time. Unfortunately, commonly used approaches, including supervised and self-supervised learning with either soft or hard feasibility enforcement, face inherent challenges such as reliance on expensive high-quality labels or difficult optimization landscapes. To address their trade-offs, we propose a novel framework that collects "cheap" imperfect labels, performs supervised model pretraining with a merit loss-based termination scheme, and finally refines the model through self-supervised learning to improve final performance. Empirical validation across challenging domains -- including nonconvex constrained optimization, power-grid operation, and stiff dynamical systems -- shows that this three-stage strategy yields faster convergence; improved accuracy, feasibility, and optimality; and up to 59x reductions in total offline computational cost. We further analyze why and when our framework improves surrogate model training, finding that (i) merit loss is an informative signal and (ii) only small numbers of cheap, inexact labels are needed to place the model in a favorable regime for self-supervised learning.
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