arXiv:2606.05029v1 Announce Type: new Abstract: Controlled experiments are the backbone of machine learning research, but at the scale of modern foundation models, they have become prohibitively expensive. Instead, the community increasingly relies on research strategies that approximate the ideal experiment at a fraction of the cost: proxy experiments and scaling laws, observational studies with publicly available models, and single-run designs that leverage variation within individual training runs. In this work, we argue that there is no free lunch when approximating large-scale experiments
The rapid scaling of Foundation Models (FMs) has led to computational and financial barriers for traditional controlled experiments, forcing researchers to adopt less rigorous methodologies.
This erosion of experimental rigor in FM research could lead to flawed conclusions, misdirected investments, and slower, less reliable AI progress, especially given FMs' pervasive impact.
The accepted methodologies for validating research in large-scale AI are being fundamentally questioned, suggesting a need for new standards or a return to more robust, albeit costly, experimental designs.
- · Well-funded research institutions
- · Companies with proprietary data and compute
- · Open-source communities focused on reproducibility
- · Independent AI researchers
- · Small AI startups relying on public models
- · AI fields requiring high experimental certainty
Increased debate and scrutiny of AI research findings, particularly for large models.
A potential bifurcation in AI research, with well-resourced entities performing rigorous experiments and others relying on less robust methods.
Slower, less certain progress in foundation model capabilities if valid experimental approaches cannot be widely adopted.
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