The Real Challenge Begins After Pretraining
teortaxesTex · x · 2026-07-14
The author notes that GPU-rich labs rarely run just one pretraining run. Instead, they parallelize several similar training tasks and advance the best performers to post-training and production.
They further point out that under this workflow, the Chinese AI industry operates with zero margin for error due to different constraints. The author also questions whether "10T-scale pretraining" is overhyped, arguing that the true difficulty lies in the compute and experimental costs required to discover an Anthropic-level training recipe.
Related event: Post-Training, Not Compute, is the Real Bottleneck for Top AI Models(2 posts)→
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