Learned, Then Lost: A Measured Single-Example Counterfactual in Pre-training
A single training example's contribution to a finished model is normally estimated rather than measured, because measuring it takes two expensive full pre-training runs that differ in one row of one batch. We ran that counterfactual 24 times at a small scale. We trained 32 GPT-2 models at 124M parameters from scratch on OpenWebText, over four conditions and eight seeds. At step 200 of 9,536, at peak learning rate, we replaced one row of a 256-row batch with a fixed context injection carrying a 194-token passage. The three injected conditions are: 1. fluent prose with a corpus-attested subject, 2. fluent prose with a fabricated subject matched to it within 0.14% on full-batch gradient delt...
| Authors | Zachary Speck, Asa Shepard |
|---|---|
| Primary category | cs.LG |
| Published | 19 August 2026 |
| Last updated at source | 7 September 2026 |
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