Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives

arXiv:2606.14870 · hep-ph, cs.LG · Submitted 2026-08-19 · Read on arXiv

Listen

Radio episode about this paper

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives".

Jane: The paper was written by Ibrahim Elsharkawy, Joschka Birk, Gregor Kasieczka, Vinicius Mikuni, Wahid Bhimji et al. from University of Toronto and Vector Institute, Toronto, ON, Canada and NERSC, Lawrence Berkeley National Laboratory, Berkeley, California, USA and Institute for Experimental Physics, University of Hamburg and Nagoya University and Kobayashi-Maskawa Institute and NERSC, Lawrence Berkeley National Laboratory and Department of Particle Physics and Astrophysics at Stanford University and Fundamental Physics Directorate at SLAC National Accelerator Laboratory.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Title: Tom: So, let's start with the title itself; "Pre-Training for Simulation-Based Science" tells us exactly what we’re doing here—we’re optimizing the learning phase of AI using simulated physics data.

Jane: And looking at the authors, it seems like a huge collaboration between physicists and computer scientists, which makes sense given that this is a complex fusion of methods.

Lu: The title suggests that we are treating these simulation datasets not just as input but as the primary resource to build a foundational understanding within the framework of OmniLearned.

Meng: We are basically building the "brain" for scientific AI by using these simulated jets, and we want that brain to be robust enough to handle real-world data later on.

Lalam: The implication here is that we aren're creating a bridge between abstract physics principles and powerful machine learning tools, Lalam's hope.

Summary: Tom: In the summary, the authors test three main pre-training objectives: supervised classification, flow-matching generation, and self-supervised masked particle modeling.

Jane: They are exploring how these different approaches perform when they fine-tune on two specific tasks: top jet classification and JetNet conditional generation.

Lu: It’s clear that the researchers weren't just picking one method but testing all seven possible combinations of these objectives to see what works best for a comprehensive model.

Meng: That approach is very practical; by testing all the modes, they can identify exactly which configurations are most robust across different operational parameters.

Lalam: The fact that no single objective wins across the board tells us that nature, or in this case physics data, is much more complex than a simple single-signal model suggests.

Improvements: Tom: One of the most important findings relates to how much pre-training data we use; the paper shows that the optimal approach depends heavily on whether you have a large or small downstream label set.

Jane: The results suggest that when both your model and your labels are plentiful, pure supervised classification is simply the best strategy for classification tasks.

Lu: But in low-label environments, it's those hybrid methods—like combining L class with L MPM—that provide a massive boost to the performance.

Meng: That data-dependency is critical for us; if we know our downstream label scarcity, we can engineer the pre-training phase to be highly specific and efficient.

Lalam: The authors also found that combining flow matching with classification or MPM helps regularize the model's representation, preventing it from being overly specialized to one task.

Conclusion: Tom: So, we’ve seen that the right pre-training objective isn't a fixed property of one single goal but depends entirely on the joint configuration of data and capacity.

Jane: It seems like we are moving toward a future where combining multiple objectives—like flow matching and masked particle modeling—will be the standard practice for complex scientific AI.

Lu: The orthogonality they found suggests that trying to make an AI good at both generation and classification simultaneously requires incorporating all three signals into the pre-training process.

Meng: This provides a clear blueprint for how to approach these large-scale scientific AI challenges, optimizing resource use based on specific needs rather than applying a one-size-fits-all solution.

Lalam: To conclude, the "Pre-Training for Simulation-Based Science" paper is showing us that the path toward truly powerful scientific AI is not just about more data, but about intelligently blending different training signals to unlock maximum potential.

Ibrahim Elsharkawy, Joschka Birk, Gregor Kasieczka, Vinicius Mikuni, Wahid Bhimji, Benjamin Nachman

University of Toronto and Vector Institute, Toronto, ON, Canada and NERSC, Lawrence Berkeley National Laboratory, Berkeley, California, USA · Institute for Experimental Physics, University of Hamburg · Nagoya University and Kobayashi-Maskawa Institute · NERSC, Lawrence Berkeley National Laboratory · Department of Particle Physics and Astrophysics at Stanford University and Fundamental Physics Directorate at SLAC National Accelerator Laboratory

hep-ph, cs.LG

Submitted: 2026-08-19

Updated: 2026-08-20

Code: https://github.com/ibrahimEls/PretrainingForScience

Importance score: 80/100

The gist: The following is a detailed summary of the scientific paper, quoting relevant sections to ensure accuracy and fidelity to the source material: This study investigates how different pre-training

Key concepts

Pre-Training Objectives
The authors tested three main methods for pre-training: supervised classification, flow-matching generation, and self-supervised masked particle modeling. These objectives are used to build a foundational understanding in the AI model using simulated physics data.
Data Dependency
The optimal pre-training approach depends heavily on the amount of available data. When both the model and its labels are plentiful, supervised classification is best. However, when labels are scarce, hybrid methods perform significantly better.
L_{class} and L_{MPM}
These represent specific training objectives (classification loss and masked particle modeling). Combining these two methods provides a massive performance boost in environments where data labels are low, helping to regularize the model's representation.

Terminology

Summary

The following is a detailed summary of the scientific paper, quoting relevant sections to ensure accuracy and fidelity to the source material:

This study investigates how different pre-training objectives—supervised classification, flow-matching generation, and self-supervised masked particle modeling (MPM)—influence the performance of foundation models in high-energy physics (HEP) simulations. The researchers aim to disentangle the contributions of supervised, generative, and self-supervised signals to the downstream transfer while mapping how this relationship depends on data availability at both pre-training and finetuning stages.

Methodology and Study Design

The study utilizes the OmniLearned framework, which is a Point-Edge Transformer (PET) foundation model for jet physics. The researchers extended this framework by adding a Masked Particle Modeling (MPM) head, resulting in seven distinct pre-training configurations:

  1. Jet classification: Supervised cross-entropy on jet type labels (L class).

  2. Jet generation: Flow-matching with a velocity-prediction objective (L gen).

  3. Masked Particle Modeling (MPM): A self-supervised objective predicting masked jet constituents (L MPM).

The total pre-training loss is calculated as a sum over the active head losses, allowing for all pairwise combinations of these objectives to be studied. The experiments were conducted across three key axes:

  • Model Size: Micro, Small, and Medium.

  • Pre-training Dataset Size (N pre): Swept from 10 5 to the the full 10 8 JetClass training set (JetClass).

  • Finetuning Dataset Size (N ft): Tested at low-data regimes (10 cubed, 10 4) and high-data regimes (1.2 times 10 6).

The models were evaluated on two downstream tasks: top-quark jet tagging (a classification benchmark) and JetNet conditional generation (a generative benchmark).

Key Findings: Top Jet Classification (Top Tagging)

The results for the classification task reveal that no single pre-training objective is universally optimal. The performance of the different configurations varies significantly based on data availability:

  • Abundant Data Regime: When labeled datasets for pre-training and model capacity are available, pure classifier pre-training is the best configuration at every model size. Furthermore, all classifier-containing multi-objective configurations converge to within a few percent of each other at N ft = N max.

  • Low Data Regime: In contrast, the ranking inverts in the low-label regime, at 104 top labels, Classifier+MPM is the top-ranked configuration at every model size. Even at the smallest model size, standalone MPM outperforms pure classifier pre-training despite using no labels at all.

  • Scaling and Efficiency: The study found that standalone MPM is the most scale-efficient single-objective configuration with respect to model capacity, with a slope roughly 2× that of the pure classifier, although the latter remains the most performant overall.

  • Generator Weakness: A consistent finding was that pure generator pre-training is the weakest performing configuration throughout, at every N ft, model size pair it either matches or underperforms the from-scratch baseline.

Key Findings: JetNet Generation (Jet Generation)

For the generative task, specific alignment between pre-training and downstream objectives was critical:

  • Generative Necessity: The study found that flow matching must be in the pre-training objective to see a significant finetuning advantage for generation.

  • Alignment is Key: All models that were pre-trained at least partially on the generative task show a performance gain when fine-tuned to jet generation on another dataset. Furthermore, neither the Classifier nor the MPM task help for downstream generation quality, unless combined with the generative task during pre-training.

  • Synergy: The combination of multiple objectives was found to be beneficial: The combination of multiple objectives... provides a balance in benefits from both downstream tasks.

General Conclusions and Observations

The research offers several overarching conclusions regarding the nature of foundation model transferability:

  1. Pre-training Loss is Unreliable: Pre-training loss does not reliably track downstream performance. Instead, downstream performance peaks well before pre-training converges.

  2. Data Dependence: The value of additional pre-training data depends heavily on the target task's label availability: "The value of additional pre-training data may depend sharply on the downstream sample size, ranging from near-linear gains when downstream labels are abundant to essentially no gain (or, for the pure classifier, mild degradation) when downstream labels are scarce."

  3. Orthogonality of Tasks: The study noted a lack of alignment between the two primary objectives: This indicates that the flow matching task is not well aligned with the classification task, and that the learned representation can not be easily transferred to the classification task. However, this misalignment can be mitigated by combining generation with MPM.

  4. Optimal Configuration: The study concludes that the optimal pre-training objective is not a property of the model alone but of the joint configuration of model capacity, pre-training data, and downstream data availability and compatibility.

Improvements for AI systems

Based on the performance curves presented in Figures 18, 19, and 20, which demonstrate critical scaling laws regarding dataset size and training epochs in complex physics simulations, I propose three major improvements to enhance the robustness and data efficiency of scientific generative AI systems.

The figures clearly show a near-linear performance benefit when scaling the training dataset from small (10k) to medium (100k) to large (550k) datasets. This suggests that the model is highly sensitive to data volume and that simply collecting more data is insufficient; the system must adapt its training regimen as data scales.

Specific System Improvement: Implement a Progressive Generative Augmentation (PGA) Module. Instead of relying solely on a fixed, static dataset size, the system should dynamically incorporate synthesized data generated by its own most robust component (e.g., the Generator or MPM) during subsequent training epochs.

What the Improved AI System Can Do:

  1. Maintain High Performance with Limited Real Data: By using synthetic data tailored to fill known feature gaps (e.g., rare jet configurations, low-probability eta regions), the system can achieve performance levels comparable to those trained on massive real datasets (550k) while operating in environments where data collection is costly or impossible (e.g., 10k initial dataset).

  2. Self-Correction and Bias Reduction: The system actively identifies its weakest classification regions (where the difference between W 1(NJetNet) and W 1(550k) is largest) and directs the Generator to synthesize samples specifically within those failure modes, rapidly reducing model bias without requiring physical re-runs.

  3. Quantitative Metric: The system's performance metric should track the Convergence Gap Reduction Rate (G): G = W 1(PGA) - W 1(Initial Data) over W 1(550k). A higher, sustained G indicates superior data efficiency.

The performance curves across all metrics (Jet energy W 1, Particle (p T,i) W 1, etc.) demonstrate that initial performance gains are rapid but plateau at later epochs. The optimal training schedule is not simply train for 500 epochs.

The comparison between various model combinations (Classifier to Generator to MPM to Classifier + Generator + MPM) suggests that the optimal performance is achieved by combining these components, but the order and weighting of their interactions are critical.

Sources

Related papers