No single pretraining objective dominates across Inertia-1's task families

Answers part of question-inertia-1-which-design-choices-win. Benchmarking 10 pretraining objectives — autoregressive, contrastive, self-distillation, masked-reconstruction, and domain-specific methods — against each other on the same pretraining data, the Inertia-1 paper (arXiv:2607.06617) finds "no single objective dominates all settings," though "specialized or predictive objectives are generally more robust for short-term recognition tasks than generic augmentation-based baselines, while these differences are less pronounced in disease prediction."

The paper quantifies how much objective choice actually matters with "SSL variance" — the AUROC gap between the best- and median-performing SSL objective within a task family — and the gap widens with task difficulty: 2.5 for human activity recognition (HAR), 11.0 for freezing-of-gait (FoG) detection, 10.0 for disease prediction (Table 6). In the paper's words, "basic HAR is closer to saturation, whereas clinical and disease-oriented tasks still benefit substantially from representations that capture subtle motion and physiological signatures" — so picking the right SSL objective matters little for HAR but considerably more for the harder tasks.

Scope caveat, in the paper's own words: the finer-grained per-axis ablations (sampling rate, window, placement, modality, data scale) were run only on its two "top-performing representative methods," ART and PatchTST — see claim-inertia-1-data-scale-beats-model-size for that caveat in full. The 10-objective benchmark itself is not subject to that narrowing.