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arxiv: 2606.19781 · v1 · pith:X5WVKWM6new · submitted 2026-06-18 · ✦ hep-ex · cs.AI

Towards Engineering Scaling Laws with Pretraining Data Composition

Jan-Lucas Uslu , Kevin Greif , Daniel Whiteson , Benjamin Nachman This is my paper

Pith reviewed 2026-06-26 15:31 UTC · model grok-4.3

classification ✦ hep-ex cs.AI
keywords neural scaling lawspretraining data compositionhadronic jet classificationparticle physicsmachine learningscaling exponentsdata alignmentsynthetic data
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The pith

Composing pretraining data with greater diversity and task alignment shifts neural scaling laws in jet classification toward needing more data rather than larger models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Neural scaling laws show how performance grows as a power law with model size and data volume. The paper examines this in the domain of classifying hadronic jets from high-energy particle collisions, where cheap simulators make synthetic data abundant. It establishes that the exponents governing these power laws can be altered by the choice of pretraining data. When that data is selected for higher diversity and closer match to the downstream task, the scaling tilts so that gains come more from additional data than from bigger models. A reader would care because this opens a route to allocate compute differently when data is inexpensive to generate.

Core claim

For the task of classifying hadronic jets produced in collisions of high-energy particle beams, the scaling behavior can be engineered towards requiring more data rather than larger models by inclusion of pretraining data which is more diverse and better aligned with the downstream classification task.

What carries the argument

Pretraining data composition, whose diversity and alignment with the downstream classification task modify the scaling exponents that relate performance to model size versus dataset size.

If this is right

  • Jet classification performance can be improved by scaling dataset size rather than parameter count when pretraining data is chosen appropriately.
  • High-fidelity simulators enable deliberate engineering of the pretraining set to control whether data or models dominate the scaling regime.
  • The same power-law form persists, but the relative strength of the data-size and model-size terms becomes tunable through data selection.
  • Resource allocation in physics machine learning can favor data generation over model enlargement under the right pretraining composition.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same data-composition lever could be tested on other downstream tasks that also possess cheap synthetic data sources.
  • If alignment proves decisive, then metrics for quantifying task alignment between pretraining and downstream sets would become useful design tools.
  • Smaller models trained on carefully composed data might reach target accuracy at lower total compute than larger models trained on generic pretraining sets.

Load-bearing premise

Observed shifts in scaling exponents are produced by the diversity and alignment properties of the pretraining data rather than by differences in model architecture, training procedure, or downstream dataset construction.

What would settle it

Recompute the scaling exponents after swapping in pretraining datasets that hold diversity fixed while changing alignment (or vice versa) and observe whether the exponents stay the same.

Figures

Figures reproduced from arXiv: 2606.19781 by Benjamin Nachman, Daniel Whiteson, Jan-Lucas Uslu, Kevin Greif.

Figure 1
Figure 1. Figure 1: Scaling analysis using test loss on JetClass after scratch training (a) and QCD + res2p + [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Scaling diagnostics for the intermediate pretraining configurations. The panels follow [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Fine-tuning loss curves as a function of training steps for each model size, comparing [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original abstract

Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relationships are emerging for large models in particle physics. As with language, empirical studies show that the performance scales as a power law. However, unlike natural language or image domains, fundamental physics has high-fidelity simulators that produce synthetic data cheaply. This favors scaling regimes where additional data is cheaper than additional parameters, and allows the pretraining dataset itself to be engineered to influence the scaling. For the task of classifying hadronic jets produced in collisions of high-energy particle beams, we show that the scaling behavior can be engineered towards requiring more data rather than larger models by inclusion of pretraining data which is more diverse and better aligned with the downstream classification task.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript examines neural scaling laws for the task of classifying hadronic jets from high-energy particle collisions. It claims that the scaling behavior can be engineered to favor increases in dataset size over model size by composing the pretraining dataset from data that is more diverse and better aligned with the downstream classification task, taking advantage of inexpensive synthetic data generated by high-fidelity simulators.

Significance. If the central empirical result is shown to hold after isolating data-composition effects from other variables, the work would be significant for particle-physics applications of large models. It would demonstrate a practical route to shifting scaling exponents in a domain where data generation cost is low, thereby informing compute allocation decisions that differ from those in language or vision domains.

major comments (2)
  1. [Abstract] Abstract: the claim that scaling can be engineered 'towards requiring more data rather than larger models' by data diversity and alignment is presented without any reported scaling exponents, performance metrics, error bars, or quantitative comparison between regimes.
  2. The attribution of observed scaling shifts to pretraining-data properties requires that model architecture, optimizer, training schedule, tokenization, and downstream dataset construction remain fixed while only data composition is varied. No description of such controls appears in the manuscript, so the causal link between data properties and the reported change in scaling behavior cannot be assessed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that scaling can be engineered 'towards requiring more data rather than larger models' by data diversity and alignment is presented without any reported scaling exponents, performance metrics, error bars, or quantitative comparison between regimes.

    Authors: The abstract is intended as a concise summary. Detailed scaling exponents, performance metrics with error bars, and quantitative comparisons across data-composition regimes are reported in the results section and associated figures. To address the concern, we will revise the abstract to include key scaling exponents and a brief quantitative comparison between regimes. revision: yes

  2. Referee: The attribution of observed scaling shifts to pretraining-data properties requires that model architecture, optimizer, training schedule, tokenization, and downstream dataset construction remain fixed while only data composition is varied. No description of such controls appears in the manuscript, so the causal link between data properties and the reported change in scaling behavior cannot be assessed.

    Authors: We agree that explicit documentation of controls is required to establish the causal attribution to data composition. In the experiments, model architecture, optimizer, training schedule, tokenization, and downstream dataset construction were held fixed while only pretraining data composition was varied. We will add a dedicated subsection in the methods describing these fixed controls to make the isolation of the data-composition variable explicit. revision: yes

Circularity Check

0 steps flagged

No circularity detected; empirical observation with no reduction to inputs or self-citations

full rationale

The paper presents an empirical study claiming that pretraining data composition (diversity and alignment) can shift scaling exponents to favor data over model size in jet classification. The abstract and provided text contain no equations, fitted parameters, or derivations that would make the claimed result equivalent to its inputs by construction. No self-citations are invoked as load-bearing premises, uniqueness theorems, or ansatzes. The central claim is framed as an experimental finding rather than a mathematical prediction or renamed known result, rendering the derivation chain self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no information on free parameters, background axioms, or new postulated entities.

pith-pipeline@v0.9.1-grok · 5665 in / 996 out tokens · 25697 ms · 2026-06-26T15:31:34.276109+00:00 · methodology

discussion (0)

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