arXiv：cs.LG· Oz Amram, Darius A. Faroughy, Tjarko Gerdes, Anna Hallin, Gregor Kasieczka, Michael Kr\"amer, Humberto Reyes-Gonzalez, David Shih·· 4 小时前AI 评分35
粒子喷注生成的神经缩放定律研究
Neural Scaling Laws for Jet Generation
AI 导读
研究首次探索粒子喷注生成任务中是否存在缩放定律，成功复现了模型规模的对数缩放行为。团队还发现切片 Wasserstein 距离与下一 token 预测验证损失单调相关，表明该损失可作为物理性能的良好代理指标；但数据集规模和计算量的缩放行为明显较弱，作者通过引入可学习窗口概念分析这一现象，认为自回归预测在喷注组分上比语言模型更早饱和。
正文
Abstract:Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- are modified. Extracting these scaling laws informs the training of large complex models for which the tuning of hyperparameters in traditional ways is not feasible. This work for the first time explores if scaling laws can also be observed for the task of particle jet generation -- both relevant as a pre-training objective for foundation models and as in-situ simulation by itself. We indeed replicate the key logarithmic scaling law behavior for model-size scaling. Beyond studying the next token prediction validation loss of the generative model, we also study the sliced Wasserstein distance of five physical quantities that are not immediately available to the model during training. Our study shows that this quantity is monotonically related to the next token prediction validation loss, meaning that this loss is indeed a good proxy for the physics performance. For the scaling with dataset size and compute, we observe substantially weaker scaling behavior of both the loss and the sliced Wasserstein distance. We analyze this behavior by introducing the concept of a learnable window, and argue that autoregressive next token prediction on jet constituents exhibits comparatively rapid saturation relative to language-model studies. We discuss possible origins of this behavior, including the stochastic nature of QCD radiation and differences between generative and supervised learning tasks in collider physics.
| Subjects: | High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); Data Analysis, Statistics and Probability (physics.data-an) |
| Report number: | TTK-26-17 |
| Cite as: | arXiv:2605.28940 [hep-ph] |
| (or arXiv:2605.28940v2 [hep-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28940 arXiv-issued DOI via DataCite |
Submission history
From: Anna Hallin [view email]
[v1]
Wed, 27 May 2026 18:00:03 UTC (106 KB)
[v2]
Tue, 6 Oct 2026 13:14:12 UTC (106 KB)
来源：arXiv：cs.LG · arxiv.org