The fraction of compute used for weight updates will decline rapidly if models are trained on ML capabilities or safety research environments. Currently, in a SWE RL environment, the sandbox where the agent runs accounts for around 1-5% of the total cost (for Kimi K3-class models), with the rest going to forward and backward passes. If companies start training on environments that closely resemble ML workflows (both safety and capabilities, think mean TH ~ 40 hours, H100 class GPU), the compute used to run experiments during rollouts could account for a large fraction of RL compute (>50% in some cases), even when these tasks have a high pass rate and only make up 1% of the environment mixture.