Compute, data, and parameter count are the three logarithmic values that correspond to model performance.
So if you find ways of improving training efficiency (the simplest conceptually is probably local/global sliding window attention like Gemma; the latest DeepSeek and Kimis have quite fancy ones), you will be able to have more effective compute, and train smarter models while keeping data and param count the same.
If you find this fascinating and want to dive more, I encourage checking out nanochat (solid GPT-2 repro; minimal LoC); and trying to make architectural tweaks with a coding agent.
“Learning through experimentation” has never been easier or more fun in my opinion. If you have a GPU, even an older 8GB card (or a few bucks for Runpod), you can pretrain your own small language model and start experimenting / tweaking it.
I don't get how changing the attention head (i.e sliding window attention instead of vanilla transformer) is only seen as a "compute" improvement. Indeed, it improves the complexity from quadratic to linear, but there was a reason why people used transformer and not sliding windows for so long (they were already there in 2021).
Making sliding window attention work with performances on par with vanilla transformers is a non trivial problem and is not solved by only putting more compute. This still requires "smart" ideas in that direction.