Data attribution methods used in practice (TRAK, LoGRA, EK-FAC) are post-hoc: after training they make a second pass over the training set to recompute per-sample gradients, repeated per checkpoint when ensembled. Traceprop avoids that pass by recording projected per-sample gradients inline on the training backward pass. A Kronecker-factored sketch scales from a single tracked layer to every layer without materializing a dense projection matrix. On LoRA fine-tunes of GPT-2 and Pythia models up to 2.8B on one NVIDIA L4, inline logging costs 0.30-1.08% of wall-clock time at last-block scope and stays under 1% (0.79%) even when tracking every layer of Pythia-1B. Against LogIX, the closest inline-capable competitor, the factored sketch is 2.0-4.1x cheaper at equal storage, a gap that grows with tracked scope and is significant at every scope tested, while matching or exceeding LogIX's attribution quality at matched storage. Building the attribution-ready store inline is 60-242x cheaper than one post-hoc pass and 301-1211x cheaper than a five-checkpoint TRAK ensemble, with recorded gradients matching autograd exactly. Because each stored gradient carries source-file lineage, the same pass also produces EU AI Act Article 26 audit trails.

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