Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining

Measuring training data influence consistently across language model pretraining is challenging. It is difficult to select downstream tasks or validation sets representative of a model's general capabilities, and reliance on task performance at intermediate checkpoints complicates comparisons across training. We propose a measure of training data influence that does not require selecting a downstream task or validation set as the attribution target. Specifically, we define an example's influence by how much its gradient update reduces the squared distance to the final parameters of a given pretraining run, and estimate this quantity from intermediate checkpoints without retraining. Applying the method to 18 configurations from the Pythia and PolyPythia suites, we find systematic temporal changes in influential data. Early in training, literature-related data are more strongly aligned with the trajectory toward the final parameters, whereas STEM data become more strongly aligned in later stages. This qualitative crossover is broadly consistent across model configurations. Our results provide a tractable trajectory-level view of how influential data change throughout pretraining, complementing influence analyses defined with respect to specific downstream tasks or validation sets.

Wiring Beats Blending: What Transfers Between Transformer Sizes -- and What Doesn't

Model families are typically trained size by size, each from scratch. Can a pretrained large model instead be converted into a smaller sibling? We characterize the 1.4B->410M conversion in Pythia end to end. Representations align strongly across sizes (ridge R^2=0.84) while parameters align weakly. Dense weight projection is functionally destructive, and a bit-exact control shows this is not an assembly artifact: basis mixing breaks rotary, per-head, GELU, and LayerNorm structure. After the best-fit linear operator, weight residuals are statistically indistinguishable from noise under shuffle controls. Conversion value therefore lives in initialization. In matched-budget continued pre-training we decompose conversion into two independent levers: least-squares compensation (function lever, best zero-shot) and variance-preserving rescale (dynamics lever, best endpoints). Compensation is a token-efficient, low-budget win rather than a universal one. At 30M tokens it beats the strongest subcloning variant on both a width-reduced pair (84.0 +/- 1.8 vs. 89.7 +/- 3.7, 3/3 seeds) and a held-out depth-reduced pair (109.3 vs. 117.9, 3/3 seeds), reaching a given quality with fewer tokens. At a 33x larger budget the two converge to parity (40.0 vs. 40.0), both far ahead of from-scratch, which transfer initialization always beats: by up to 18x at low budget, with the margin narrowing at convergence and at the largest scale. We also map the method's boundary. At about 5x the donor scale (6.9B->1.4B) stacking both levers over-corrects, consistent with ill-conditioning of the compensation solve at large width, which points to dimension-aware regularization as a fix. At matched budget our initialization also beats structured pruning with distillation, the standard pipeline, and improves further combined with it. Code, checkpoints, and the frozen evaluation corpus are released.