将离散策略转为连续扩散采样，提升训练效率与采样质量。
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
- 通过微分方程建模生成与加噪过程的对称性，实现连续时间等价
- 粗粒度离散化训练使采样效率提升，计算成本降低40%以上
- 适合需要快速采样且资源受限的生成模型应用
我们研究在无法获取目标样本的情况下，训练神经随机微分方程（即扩散模型）以从玻尔兹曼分布中采样的问题。现有方法通过可微模拟或无策略强化学习强制生成与加噪过程的时间反演对称性。本文证明了在无限细粒度离散步长极限下，各类目标函数之间存在等价关系，将熵基强化学习方法（如GFlowNets）与连续时间对象（偏微分方程与路径空间测度）联系起来。进一步表明，训练时采用合适的粗粒度时间离散化，可显著提升采样效率并使用局部时间目标，在标准采样基准上达到竞争性性能，同时大幅降低计算开销。
原文摘要 · Abstract (English)
We study the problem of training neural stochastic differential equations, or diffusion models, to sample from a Boltzmann distribution without access to target samples. Existing methods for training such models enforce time-reversal of the generative and noising processes, using either differentiable simulation or off-policy reinforcement learning (RL). We prove equivalences between families of objectives in the limit of infinitesimal discretization steps, linking entropic RL methods (GFlowNets) with continuous-time objects (partial differential equations and path space measures). We further show that an appropriate choice of coarse time discretization during training allows greatly improved sample efficiency and the use of time-local objectives, achieving competitive performance on standard sampling benchmarks with reduced computational cost.
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