Obviously building a big high dimensional language model is hard yes okay.
But once we have one can’t we just jiggle weights and run tests? why can’t I just download a program to “evolve” my language model?
“Am I just stupid and this is just too trivially easy to be a program?”
peace
there’s lots of different kinds of RL algos with different requirements
In general though, the tradeoff you’re making is: data efficiency vs compute complexity
On one end, evolutionary methods & gradient-free optimization methods are simple, but data hungry.
On the other end, are things like model based RL (eg building reward models to train your generator model) are more data efficient, but are more complex since they have more moving parts and more live models to train.
So to answer:
No, on the model-based end, you’re training at least 2 models: the generator and the reward model.
On the evolutionary & gradient free end, you need far more data than supervised learning, since reinforcement learning doesn’t tell the agent what to do at every time step, only after N time steps, so you’re getting basically 1/Nth the training signal for each step compared to supervised learning.
Basically, we as GPU poors are in the wierd position where anything we can train under these limitations would probably have worse performance than just training a larger model off supervised datasets