Flaw in anti-finetuning: Cost > Quality once models are saturated
rhythmrg · x · 2026-08-26
Cyrus critiques the skepticism around fine-tuning by arguing that viewing "good enough" as an endpoint rather than a price is a flaw. Once a task is saturated with quality, serving cost and latency become the primary differentiators. As smaller models improve alongside the frontier, post-training techniques like fine-tuning allow users to deploy smaller, cheaper models that meet specific domain requirements. This specialization creates checkpoints that frontier labs will not ship directly to users.
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