Reinforcement learning from verifiable rewards

Outcome-only GRPO squeezed a toy model's answers onto about 2.5 of 99 possible sums

Greedy accuracy came back to baseline by 1,500 steps and per-sample correctness tripled, so the fall in pass@64 to 0.19 only surfaced when the authors paid for 64 samples a problem instead of one.