kalomaze argues ensembled synthetic data can cover data-scarce domains
kalomaze · x · 2026-10-15
kalomaze defends synthetic-data training on X with two core claims: no two distinct problems should be expected to be wholly disjoint in the representational sense, and there is no serious reason to believe ensembled synthetics can't powerfully overlap the span exercised by a specific domain that 'lacks data'. A direct rebuttal to the common objection that some fields simply can't be trained for.
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