RLCD and the Jev moment
in my training-data primer, i argued that synthetic data could scale expert judgment, but couldn’t replace it when the work got ambiguous. Jev has me wondering how much of that judgment needs to show up as individual training examples.
Jevyou give Jev context and a question with defined answers. for a support ticket, that might be which team should handle it. Jev returns probabilities for the possible answers, which the surrounding code can use to route the ticket or ask for a review.
TypeSafe calls its training approach RLCD, reinforcement learning for calibrated decisions. the goal is for answers assigned an 80% probability to be right about 80% of the time across many predictions. that makes uncertainty something a developer can use when deciding how much responsibility to give the model.
and its founder says Jev was trained entirely on synthetic data. i’d still like to see the reward and training loop behind that. the public explanation describes the goal much more clearly than the process that produces it.
says Jev was trained entirely on synthetic datamaybe i made “synthetic” do too much work. if experts design the cases and the criteria used to judge them, their judgment could reach the model through generated examples. i want to understand how far that can go when the decisions get messy.
Jev is named for Jevons paradox, where greater efficiency can lead to more total use. if decisions get cheap enough to wire into every workflow, i’d expect the work of defining and checking them to grow too. that could change labor pretty dramatically, especially if a small amount of expert input ends up shaping a huge number of decisions.
