What does this equation mean?

Two data points three years apart, $\rho$ $\approx$ 0.018 for InstructGPT and $\rho$ $\approx$ 0.17 to 0.19 for DeepSeek-R1, describe a ratio that has grown roughly tenfold. Epoch AI’s broader analysis of post-training compute trends confirms the direction independently of any single model: reasoning-focused post-training compute has scaled by roughly a factor of ten every four months, far outpacing pretraining’s historical growth of roughly four to five times a year, and the analysts note this cannot continue much longer, because tripling post-training compute is on a path to soon mean tripling the entire training budget [ 10 ] . That finding needs one careful qualification for an article about safety economics specifically: the reasoning-focused reinforcement learning driving most of that growth is aimed chiefly at capability — solving verifiable math and coding tasks — not at preference tuning for helpfulness or harmlessness. No major lab separately discloses what share of its post-training compute is safety-specific RLHF or RLAIF as against capability-focused reasoning RL. That non-disclosure is itself a real limit on what this article, or anyone outside these labs, can account for: the alignment share of $C_{\mathrm{RL}}$ is not a number anyone outside the labs can currently observe, only bound from above by the published total.