alt Hacker NewsThe combination approach jives well with my use of the models in a number of areas. I guide models to use best-in-class algorithmic approaches as available. (Eg using constraint solves for a particular problem where pure Monte Carlo rarely gives "in-bounds" data.)
I find it odd that frontier models often don't suggest the most powerful methods for crushing problems, but it may be that the training data doesn't actually have "good enough" experts on the problems I encounter. If the experts don't know about the best ways to solve the problem, they'll get dinged in training for even trying.
Do you enumerate the options of the algorithms to the models? I've tried to do "algorithmic discovery" with these systems, e.g. openevolve, and to be honest the models didn't really focus on that part.
Instead they were focusing more on optimizations of the existing algorithm that has been implemented. Maybe it's an artifact of the problem I was throwing to them (I was asking to optimize the implementation of select_k in Arrow, which is currently using a max-heap streaming algorithm).
I've started documenting my journey with this here: https://www.kostasp.net/posts/16-ai-experiments-apache-arrow in case you want to take a look. Any advice would be highly appreciated, I'm looking for more inspiration on how to torture myself with that stuff.