Pretraining may explain more of LLM math skill than reward signals
The case against treating verifiable rewards as the main explanation for LLM math and coding ability is that strong programming data can generalize to tasks the model has not seen directly. The writeup points to Bel apparently finding a Navier-Stokes solution without having trained on that solution, while noting that reinforcement learning can still teach models genuinely new things. For readers evaluating model progress, the takeaway is to look at the training corpus, not just the post-training recipe.