it helps when you first start by distilling (full distribution) domain specific workflows from a larger open model (kimi k3 would be perfect for this now, we used glm 5.2) and then apply your own private RL pipeline to the result. we did this to get a private 27B model for English -> SQL and the results were better than Fable, it lost the ability to explain however.

beating frontier models on highly specialized domains with a tiny fraction of the cost is not difficult to achieve, even when you don't have your own RL pipeline such distillation can still have major savings (27B model will become almost as good as a 3T model on the task). it probably won't work if the task is so ambitious as to not be suited for a specialist.