I don’t think we disagree on many of the major points in your comment. But your original claim was:
if moving a pass@k single-step capability to pass@1 is all RL does, even improvements on multi-step tasks still hit a ceiling soon, even if that ceiling is exponentially higher than the ceiling of single-step performance improvement. And it’s not clear that this potential exponential improvement actually unlocks any transformative/superhuman capabilities.
The claims in the paper are agnostic to the distinction between the neural network and the algorithm learned by the neural network. It simply claims that RL makes models perform worse on pass@k for sufficiently k—a claim that could follow from the base models having a more diverse distribution to sample from.
More specifically, the paper doesn’t make a mechanistic claim about whether this arises from RL only eliciting latent computation representable in the internal language of the learned algorithm, or from RL imparting capabilities that go beyond the primary learned algorithm. Outcome-based RL makes the model sample possible trajectories, and cognition outputting trajectories that are rewarded are up-weighted. This is then folded into future trajectory sampling, and future up-weighted cognition may compound upon it to up-weight increasingly unlikely trajectories. This implies that as the process goes on, you may stray from what the learned algorithm was likely to represent, toward what was possible for the base model to output at all.
I agree that if all RL ever did was elicit capabilities already known by the learned algorithm, I agree that would top out at pretty unremarkable capabilities (from a strong superintelligence perspective—I disagree that the full distribution of base model capabilities aren’t impressive). But that’s very different from the claim that if all RL ever did was move a pass@k capability to pass@1, it implies the same outcome.
FYI, I couldn’t click into this from the front page, nor could I see anything on it on the front page. I had to go to the permalink (and I assumed at first this was a joke post with no content) to see it.