Other reported training costs tend to include rental of the cloud hardware (or equivalent if the hardware is owned by the company), e.g. NVIDIA H100s are sometimes priced out in cost-per-hour.
It would be simply wrong to exclude the staffing costs. When each engineer costs well over 1 million USD in total costs year over year, you sure as hell account for them.
Calculating the cost in terms of GPU-hours is a whole lot easier from an accounting perspective.
The papers I've seen that talk about training cost all do it in terms of GPU hours. The gpt-oss model card said 2.1 million H100-hours for gpt-oss:120b. The Llama 2 paper said 3.31M GPU-hours on A100-80G. They rarely give actual dollar costs and I've never seen any of them include staffing hours.
As with staffing costs though it's hard to account for these against individual models. If Anthropic run a bunch of training experiments that help them discover a new training optimization, then use that optimization as part of the runs for the next Opus and Sonnet and Haiku (and every subsequent model for the lifetime of the company) how should the cost of that experimental run be divvied up?