alt Hacker NewsMy point with the force field example wasn’t to argue against neural scaling as a valid strategy, it totally is effective and a lot of groups are doing it. But I feel like we might be talking past each other a bit.
What I’m pushing back on is what I think is a sort of one-dimensional view of Sutton’s bitter lesson. People seem to equate it with model scaling, but there are lots of general ways to leverage computation that don’t involve just scaling models and supervised training datasets up. For example Sutton’s first example is straight up search, no parameters at all.
The point of the force field example is that it seems you don’t need billions of parameters to represent the functions we’re interested in, but with small models it’s harder to find those functions by pushing harder on the standard training algorithms, and that maybe some different algorithm that leverages computation more effectively could do so.
I totally agree with what you're saying. Your point about other methods to leverage computation is true and important.
But that's exactly my claim: Smaller models can win on the computational efficiency front, but not the overall capability front. And as long as compute is getting cheaper, investing in more efficiency while there are still major capability on the table isn't a good business strategy.
Smaller, efficient models could lead to some really interesting things though especially considering it could lead to some Jevon's paradox like moment. To be honest, I feel like the biggest issue in regards to LLM usage in practice is that the patterns of use aren't really well developed. Yes we have agents, but it's still somewhat unclear what an agent can "do" - People seem to mostly focus on replacing some kind of existing process with an agent driven one, but actually coming up with AI-native processes is way harder.