I am assuming that we agree that training data is not some fundamental necessity, historically, once you have the right learning algorithm, you can discard all training data and just train from scratch (e.g. AlphaZero).
More concretely on today’s AI paradigm, RLVR is already a huge part of training, and works well for tasks that can be easily verified. (Or do you foresee pretraining specifically becoming data bottlenecked in a way that somehow cannot be compensated for by more RL?)
By “software intelligence explosion”, do you mean training better and better AI researchers (which is something well amenable to RLVR, so no training data needed) and doing RSI via that? And so you are talking about the scenario where for some reason that does not happen?
For which specific part of training, for what kind of tasks, do you see data becoming a bottleneck?
Trying to understand your model:
I am assuming that we agree that training data is not some fundamental necessity, historically, once you have the right learning algorithm, you can discard all training data and just train from scratch (e.g. AlphaZero).
More concretely on today’s AI paradigm, RLVR is already a huge part of training, and works well for tasks that can be easily verified. (Or do you foresee pretraining specifically becoming data bottlenecked in a way that somehow cannot be compensated for by more RL?)
By “software intelligence explosion”, do you mean training better and better AI researchers (which is something well amenable to RLVR, so no training data needed) and doing RSI via that? And so you are talking about the scenario where for some reason that does not happen?
For which specific part of training, for what kind of tasks, do you see data becoming a bottleneck?