[D] What would you recommend testing new general approaches (architectures/optimisers) on?

A lot of my work so far has been on optimisers and architecture, but have only ever tested them on small token prediction language tasks when publishing findings. What would you need to see to be convinced that a novel general approach was truly superior? Specific datasets and model sizes and relevant benchmarks would be extremely appreciated.

architecture architectures datasets ever general language machinelearning novel prediction publishing small tasks testing them token work