When Do Updates Matter? Counterfactual Update Redundancy in 4D Gaussian Splatting

Dynamic Gaussian splatting optimizes tens of thousands of explicit primitives throughout training, yet the value of individual optimizer updates remains poorly understood. We introduce a counterfactual measurement framework that evaluates the one-step value of a candidate Gaussian update from a fixed training state using training-only probe views and exact rollback. On 4D Gaussian Splatting, update utility becomes sharply concentrated in mid-to-late training: the top 25% of candidate updates account for 97.2–99.8% of positive utility. Broader probe averaging improves identity agreement, but does not recover a context-invariant ranking: from 1 to 24 probe views, mean cross-probe Spearman rises from 0.019 to 0.381 while top-25 positive-utility coverage remains 96.9–100%. Under strict post-densification fixed-identity comparisons across five result-blind paired Train-Opt contexts, temporal rank correlation remains near zero (mean Spearman 0.010 ± 0.020, range −0.018 to 0.036). In a 48-cell stage-by-budget study normalized by empirical three-seed Full-run variation, 33/48 cells satisfy the frozen selection-sensitive rule and 20/48 are budget-starved; across ten threshold-adjacent cells, the sensitive/non-sensitive classification is consistent across all three seeds in 9/10 cells. Removing the systematically weak Motion policy reduces selection-sensitive cells from 33/48 to 27/48 and median policy spread from 1.590 to 0.359 dB. Targeted semantic bridges on both 4D-GS and Deformable-3D-GS further show that selective optimizer updates and active-only backward are different interventions because shared deformation gradients couple primitives. The concentration and path-dependence patterns also recur on the second backbone. Together, the experiments show that one-step utility is highly concentrated, while sustained selective-update outcomes are separately shaped by scene, stage, sparsity path, and shared-gradient execution semantics.