01RELEVÂNCIA GLOBAL
Pós-treino vertical sai do laboratório
Harvey apresenta um modelo jurídico pós-treinado sobre uma base aberta; David Sacks e Aaron Levie leem o caso como evidência de que domínio, dados de fluxo e eficiência de inferência podem sustentar produtos especializados.
A tese não é que todo problema pede um modelo próprio. É que tarefas repetitivas, caras e bem compreendidas criam espaço para otimizar simultaneamente qualidade e custo.
Great post on what post training looks like for applied AI use-cases to bring down costs and improve accuracy on certain tasks. This will increasingly be an approach that companies that can get closer to the underlying workflow in an enterprise will take. The key is that once you understand a domain well enough and have enough volume on a set of similar tasks, it can start to make sense to purpose design models just for that work. “In post-training, we incentivized efficient tool use and reasoning through reward shaping, preferring trajectories that would reduce tokens consumed at inference-time given equivalent performance. This allowed us to co-optimize for both cost and quality, gaining significant performance while keeping cost stable.” Now, this won’t make sense in every domain, as general purpose frontier closed or open models will be good enough out of the box -or necessary- for the work at hand. But once you have deep enough vertical expertise, and either the costs are too high to do at scale *or* you have a unique enough task type not being trained on otherwise, this will make a ton of sense. Very compelling value proposition for being an applied AI company, and awesome to see multiple paths to winning in the market right now.
