Large parameter models are way too large. They are certainly capable models, astra, Claude, kimi, but what do they have in common. They primarily have all of the knowledge to complete tasks in there “brain”, and we are seeing that there are smaller models showing capabilities of larger models that don’t need billions of parameters. There are two people who take a test, the nerd who’s been studying all night and day, and will most likely ace the exam, and the ditz, the guy who’s pretty care free but doesn’t have much going on in their head, doesn’t care about the exam at all. The professor sees the ditz and and feels bad for them, so he makes an exception, 1 sheet of paper of notes to use for the exam. Normally, the ditz would fail but with the cheat sheet their intelligence matters significantly less. No memorization needed, just a basic grasp on the fundamental concepts, right? Lets say the professor didn’t put a limit on the amount of notes used for the exam. After a certain point, how much could the ditz not need to know and just follow their notes? We are trying to make instead of a brain that knows everything, to a brain that knows how to navigate everything. The ditz of course couldn’t write their own notes, but they instead could employ someone to make the “map” for them. So here’s my idea. An rlm digs through a library or database to build a map That map is then used by a small model trained for the tool calls to probe the map The small model can then answer any questions about the information given by the map Pros of this idea Results are repeatable, because the model can change its weights as much as it needs, but the map stays the same, the avenues stay the same. The map does not need to sit in vram or ram, but in storage. The model needs to know significantly less than it needs to be trained for, and can “cheat” complete comprehension in whatever the maps contents are With a complete map, we’d have an almost exact way to train an llm to be fluent in any language, coding or spoken. Subject and skills too. The hallucinations exist within the map not on the model, if there are any. Cons: We are trading ram with storage, so however large the map is would take up space Trying to map a library/subject might take new concepts of conceptual organization to solve Probably more, but cant think of any. Excited to hear some responses. submitted by /u/FluidModeNetwork
Originally posted by u/FluidModeNetwork on r/ArtificialInteligence