无需训练模型，用扩散模型直接生成最优设计方案
ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion
- 在推理时用扩散模型直接引导生成，不依赖额外代理模型
- 51个任务中整体表现优于16种方法，优化效果接近最优
- 适合希望快速部署多目标优化的工程师和研究者
离线多目标优化（Offline MOO）旨在从静态数据集中找到帕累托最优设计，而无需与环境进行额外交互。现有生成式方法通常依赖外部代理或偏好模型进行采样引导，增加了训练复杂性且引导可能不可靠。我们提出 ParetoPilot，一种即插即用的方法，利用预训练的条件扩散模型，在推理阶段无需任何代理模型即可引导设计逼近帕累托前沿。ParetoPilot 在逆扩散过程中引入了 Infer-Perturb-Guide（IPG）引擎：首先通过对齐条件与无条件预测，推断出每个样本的个体条件目标；然后在批量内集体扰动这些目标，平衡向帕累托前沿收敛与样本多样性；最后通过标准的无分类器引导（CFG）注入扰动目标，引导生成轨迹。在51项任务上的实验表明，ParetoPilot 在16种方法中取得了最佳整体排名，并实现了具有竞争力的超体积提升。
原文摘要 · Abstract (English)
Offline multi-objective optimization (Offline MOO) seeks Pareto-optimal designs from static datasets without additional environment interactions. Existing generative methods typically guide sampling with external surrogate or preference models, which adds training complexity and may provide unreliable guidance. We propose ParetoPilot, a plug-and-play method that guides designs to Pareto front at inference time using a pre-trained conditional diffusion model without any surrogate. ParetoPilot introduces an Infer-Perturb-Guide (IPG) engine within the reverse diffusion process. IPG first infers the individual conditional target for each sample in the batch by aligning its conditional and unconditional predictions. It then perturbs these targets collectively across the batch, balancing convergence toward the Pareto front and diversity among samples. Finally, the engine guides the generative trajectory toward the Pareto front by injecting these perturbed targets via standard Classifier-Free Guidance (CFG). Experiments on 51 tasks demonstrate that ParetoPilot achieves the best overall ranking among 16 methods and competitive hypervolume improvement.
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