The methods they employ are to improve the context being given to the model irrespective of the context length. Even when the context length improves these methods will be used to decrease the search space and resources required for a single task (think about stream search vs indexed search).

I’m also curious what paper you are referencing that finds that more context vs more relevant context yields better results?

A good survey of the methods for “Augmented Language Models” (CoT, etc.) is here: https://arxiv.org/pdf/2302.07842.pdf