What does this equation mean?

That fraction has moved substantially since 2022. DeepSeek’s R1 model provides the clearest recent public case, because both its pretraining and its reinforcement learning phase were disclosed in enough technical detail for an independent estimate to be built. Epoch AI’s own reconstruction puts DeepSeek-V3’s pretraining at about 5.3 million US dollars, based on 2,048 H800 GPUs run at roughly 2 US dollars per GPU-hour over the reported training schedule, and the initial R1-Zero reinforcement learning phase at about 1 million US dollars, assuming similar hardware utilisation to the pretraining run [ 11 ] . That is roughly seventeen to twenty percent of the pretraining figure — an order of magnitude larger a share than InstructGPT’s, and it is worth being precise that this is an independent analyst’s estimate resting on assumed GPU pricing and utilisation, not a cost figure DeepSeek itself disclosed. 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.