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Asa Shepard

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#machine learning Preprint Aug 2026

What It Costs to Compose, Rebuild, and Correct Precomputed Memory

Both warm-rebuilding trained compressions of key-value caches and serving specifically-phrased updates beside a memory, as pasted text or injected cache state, show particular promise for keeping precomputed memories current, the latter as an interim measure between rebuilds.

Asa Shepard · 0 citations
#machine learning Preprint Aug 2026

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 o...

Zachary Speck, Asa Shepard · 0 citations

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