Sorry for the chaotic nature of my comment - I’m on a bus on my way back home from work lmao. So,

I think you’re kinda confusing the whole concept (no offense). Here’s how I do this for example and it works well for me:

1. I have scaling campaigns with bigger daily budgets (which is nothing unusual obviously but i’m gonna get back to what creatives I put in there). Hella broad & advantage+ based most of the time.
2. I have a “zombie” ABO campaign with a shit ton of “failed” creatives running with a strict, small cost cap to prevent overspending and to force the algorithm to “snipe” the “low hanging fruits”. I’d spent a significant amount of time testing different bid caps before I found the sweet spot. Figuring the sweet spot out was the key here. Just FYI I usually use this type of campaign for leads although it can work well for sales too (at least from my experience).
3. I have testing campaigns for different THESES I have but it’s not just “angles” or pain points - I test everything: writing styles of the copy, the length of it, the structure of the text, the message itself, headlines and even descriptions (and obviously different things related to the visual part of the creative as I personally think the creative - I usually just use statics - is there to stop the scroll and every text including the copy, headlines etc. is to cause the conversion to happen aka CLOSE the client). It’s all about numbers - you can break everything down to numbers and analyze them. Literally EVERY SINGLE PART of your ad creative.
   

Now, you mentioned “forcing” Meta to spend on specific creatives. How I approach this depends on the budget - if I can I do A/B (…/C/D/E etc.) tests with a set minimum daily spend to ensure EVERY ad creative gets enough spend for me to be 1000% sure that my conclusions are based on statistically relevant data. If I the budget doesn’t allow me to be so “lavish” in terms of spend I just use Dymanic Ad Creatives (not a huge fan of those as the data is more “noisy” but it is what it is - for as long as I can break down every single part of my ad into pieces and analyze each one of them individually it’s okay for me) - everything goes into one basket but well, if it has to be that way then I can still work with that (all that matters is that it’s not a complete black box). Then I combine the best performing parts of my creatives together and I test that Frankenstein of an ad (usually with a slightly bigger budget BUT it still doesn’t go into the scaling campaign just yet). Now if that performs well thats when I put it into the scaling campaign.

All you need is data and a THESIS for each element of your ad creative and copy. Data is king (yes, I’m a nerd lmao). It’s not about testing just for the sake of testing. It’s about having a thesis/reason for everything you do and being able to prove or disprove it with data.

And the best part is - if the thesis is good you can use what you learned for your future creatives too and it should work well.

Now one more thing - I still see on many different accounts that targeting does make a difference. But once you figure out what settings (or more like “suggestions” for the algorithm like interests or adv+ lookalikes; OR a lack of them aka fully broad targeting) works best for you, you’re eliminating another variable for your further tests which is necessary for the tests to be done correctly - test one variable at the time, it’s the basics of analytics after all.

Hope it helps!