On the Value of Tokeniser Pretraining in Physics Foundation Models
arXiv:2603.05598v1 Announce Type: cross Abstract: We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales. Training foundation models to learn the dynamics underlying such data enables the modelling of complex multiphysics phenomena, especially in data-limited settings. The emerging class of physics foundation models typically aims to learn two tasks jointly: (i) extracting compact representations of high-resolution spatiotemporal data, and (ii) capturing governing physical dynamics. However, learning both tasks from scratch simultaneously can impede the effectiveness of either process. We demonstrate that pretraining the tokeniser with an autoencoding objective prior to training the dynamics model enhances computational efficiency for downstream tasks. Notably, the magnitude of this benefit depends on domain alignment: pretrainin
arXiv:2603.05598v1 Announce Type: cross Abstract: We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales. Training foundation models to learn the dynamics underlying such data enables the modelling of complex multiphysics phenomena, especially in data-limited settings. The emerging class of physics foundation models typically aims to learn two tasks jointly: (i) extracting compact representations of high-resolution spatiotemporal data, and (ii) capturing governing physical dynamics. However, learning both tasks from scratch simultaneously can impede the effectiveness of either process. We demonstrate that pretraining the tokeniser with an autoencoding objective prior to training the dynamics model enhances computational efficiency for downstream tasks. Notably, the magnitude of this benefit depends on domain alignment: pretraining on the same physical system as the downstream task yields the largest improvements, while pretraining on other systems provides moderate gains. In-domain pretraining reduces VRMSE by 64% after 10,500 training steps compared to training from scratch. To our knowledge, this is the first systematic investigation of tokeniser pretraining for physics foundation models. We further introduce flexible spatiotemporal compression operations that extend causal convolutions to support runtime-adjustable compression ratios, enabling efficient adaptation to diverse downstream tasks. Our findings provide practical guidance for training efficient physics emulators and highlight the importance of strategic pretraining data selection.
Executive Summary
This study investigates the impact of tokeniser pretraining on physics foundation models, revealing that pretraining the tokeniser with an autoencoding objective prior to training the dynamics model significantly improves computational efficiency—reducing VRMSE by 64% after 10,500 steps compared to training from scratch. The benefit scales with domain alignment: in-domain pretraining yields the greatest gains, while cross-system pretraining offers moderate improvements. The authors also introduce flexible spatiotemporal compression operations that enhance adaptability across diverse tasks. This represents a novel, empirically validated contribution to the field, bridging a gap in understanding the role of pretraining in physics emulation. The findings provide actionable insights for researchers seeking to optimise resource allocation and model performance in data-limited environments.
Key Points
- ▸ Pretraining tokeniser improves computational efficiency
- ▸ Domain alignment amplifies benefit (in-domain > cross-system)
- ▸ New flexible compression operations extend causal convolutions for runtime adaptability
Merits
Novel Contribution
First systematic investigation of tokeniser pretraining in physics foundation models; empirically validated with measurable performance gains (64% VRMSE reduction).
Demerits
Scope Limitation
Study focuses on specific physics domains; applicability to non-physics or highly divergent systems remains untested.
Expert Commentary
The work by the authors represents a pivotal shift in understanding the architecture of physics foundation models. Historically, pretraining strategies in ML have been largely generic—applied universally without domain specificity. Here, the authors dismantle that assumption by demonstrating that the value of pretraining is intrinsically tied to the ecological fit between the pretraining data and the downstream task. This insight has profound implications: it suggests that the traditional ‘one-size-fits-all’ pretraining paradigm may be fundamentally misaligned with the nature of complex physical systems. Moreover, the introduction of runtime-adjustable compression operations is a sophisticated technical advance—it allows models to dynamically adapt without retraining, a critical feature for real-world deployment where task scope shifts frequently. Importantly, the 64% improvement in VRMSE is not merely statistical; it represents a tangible bottleneck reduction that could accelerate the deployment of physics emulators in critical infrastructure applications. This paper bridges the divide between theoretical ML and applied physics simulation, offering a blueprint for more efficient, scalable, and domain-aware model development.
Recommendations
- ✓ Adopt in-domain tokeniser pretraining as standard practice in physics foundation model development pipelines.
- ✓ Integrate flexible spatiotemporal compression modules into model architectures as modular components to enhance adaptability without increasing computational overhead.
