No, they do not point to any specific examples of novel reasoning strategies that were uncovered, nor is their sampling that extensive (at most 256 samples vs the 2048 used in https://limit-of-rlvr.github.io/ ).
Their results are consistent with novel reasoning strategies, but they're also consistent with more reliable execution of reasoning strategies that the base model can generate in principle, but rarely succeeds at due to a large number of steps. (If you have a model that can do each step independently with 99% success rate and getting the correct result requires 1000 steps, the chance of making it all the way to the end without a single error is only about 0.004%.)
I agree with your general point though. Ie, we need more thorough empirical investigation of how reasoning behavior evolves during RL training starting from the base model. And, current RL training results seem more like "amplifying existing good behavior" than "inducing emergent good behavior".
For a novel reasoning strategy, I would expect at least a few individual tokens where the base model assigns much smaller probabilities than the reinforcement-learning trained one, as opposed to just being a little smaller but spread out over many tokens. (Which would better fit a "death by a thousand cuts" scenario.)