Seq-Flow：基于自展开误差控制的高效概率预测
Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control
Yinan Huang · Shitij Govil · Bo Dai · Pan Li
中文摘要
许多科学预测任务需要在新观测到来时更新未来轨迹上的分布。传统扩散模型(generative model)与流模型从高斯噪声采样生成单次预测,通常需要大量采样步数。预热启动方法复用早期预测以降低开销,但其模型并未针对预测更新进行训练,在少步采样下会损失质量。本文提出 Seq-Flow,一种条件流模型,其常微分方程将样本从上一时刻预测分布传输到更新后的分布。由于相邻预测差异往往较小,该传输从一个信息丰富的分布起步,只需少量流评估即可得到准确的更新。递归复用也带来挑战:单次预测的误差会进入后续流的初始状态。为解决该问题,引入自展开训练,使用模型的滑动平均副本生成预测,以初始化后续训练更新。不同于将生成输出用作条件上下文的 self-forcing 方法,Seq-Flow 将其作为下一次流的源。在粒子加速器束流溢出预测任务上,Seq-Flow 在少 NFE(函数评估次数)采样预算下将 CRPS(连续等级概率评分)降低 65%,同时在流体动力学预测任务上与强基线保持竞争力。模型最多在四次更新的自展开上训练,却在超过 400 次连续更新中保持精度。代码已开源。
关键要点
- 01问题:扩散/流模型概率预测采样成本高;预热启动方法未针对预测更新训练,少步采样下质量下降
- 02方法:Seq-Flow 用 ODE(常微分方程)将样本从上一预测分布传输到新分布,并以滑动平均副本生成预测来初始化后续流进行自展开训练
- 03结果:在粒子加速器束流溢出预测上,少 NFE 预算下 CRPS 降低 65%;流体动力学任务上与强基线相当
- 04局限:训练时最多经历 4 次更新的自展开,实际部署需稳定运行 400+ 次更新
解读
尚无解读。
原始英文摘要
arXiv:2610.10440v1 Announce Type: new Abstract: Many scientific forecasting tasks require updating a distribution over future trajectories as new observations arrive. Conventional diffusion and flow models generate each forecast from Gaussian noise, often at the cost of many sampling steps. Warm-start methods reuse earlier predictions to reduce this cost, but their models are not trained to perform the forecast update itself, which can compromise quality under few-step sampling. In this work, we introduce Seq-Flow, a conditional flow model whose ODE transports samples from the previous forecast distribution to the updated one. Because successive forecasts often differ only modestly, this transport starts from an informative distribution and can produce accurate updates with few flow evaluations. Recursive reuse also creates a challenge: errors in one forecast become errors in the initial states of subsequent flows. We address this with self-rollout training, in which a moving average copy of the model generates forecasts that initialize later training updates. Unlike self-forcing methods, which reuse generated outputs as conditioning context, Seq-Flow reuses them as the source of the next flow. Experiments On particle-accelerator beam spill forecasting show Seq-Flow reduces CRPS by 65% under a few-NFE sampling budget, while remaining competitive with strong baselines on fluid-dynamics forecasting tasks. Although trained on self-rollouts of at most four updates, Seq-Flow remains accurate over more than 400 consecutive updates. Our code is available at https://github.com/Graph-COM/Seq-Flow.