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Abridged primary-source text [2]
Language models in customer-facing products must be continually updated, since the data they serve shifts over time. As retraining on the full accumulated data is impractical, each update should instead run on a small set of examples carrying the training signal of all data seen so far. The strongest current methods build this set by gradient matching, which synthesizes examples whose training gradients match those of the full data.
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- What changed
- The author-supplied record 10.48550/arxiv.2611.03496 makes an abstract and publication details available for “Online Label-Free Gradient Matching for Continual Language Model Training”. [2]
- Who should review it