To what degree would people say that so-called “reward hacking” by AI models is a conceptual descendant of what would have been called “overfitting” in the ML era?
I agree, and there's more to it. There is a entire class of problems that I call deployment-complete"reinforcement learning problems. Computer use belongs to this class.
RL works well when one can easily reset the environment to diverse enough situations, when there is a
Here's a question I find confusing and interesting and which actually tells us a lot about the nature of current AI progress:
Why has progress on computer use been so slow? Computer use is so clearly verifiable.
I think the answer is that it is not enough for a domain to be
I myself didn't fully understand what the deepseek r1 hype was about, but at some point I realized where the mismatch in hype was coming from.
For many people with reinforcement learning knowledge, the o1 announcement was pretty clear. o1 was a model trained with reinforcement
Looking back, I never understood the hype of the "deepseek moment" from last year. Distilling others' models is possible and easier than pushing the frontier. Like, nobody remembers Alpaca?