The real headline here is the collapse of the teacher-student split into a single model — a step toward self-improving post-training pipelines the field has been circling for a while. Matching GRPO, now the de facto RL baseline, at 6.5x fewer processed tokens is a meaningful efficiency claim, but a single MATH result is thin proof: math is the friendliest domain for RL gains, and transfer to code, agents and instruction-following is the actual test. If it holds at scale, the strategic upside is real — RL compute is concentrated in a handful of labs, and token efficiency is precisely the lever that lets smaller players afford serious post-training. Perplexity doing core training research alongside NVIDIA also signals the search company moving up the stack. Promising, but we'd wait for independent replication and broader benchmarks before calling it a breakthrough.