From what I know, parameter count correlates directly with capability to a certain degree. But Google has also shown there is a benefit in training that can enable lower parameter count models to match or even outperform those with bigger counts.

The trade off is in the cost for training. But in return, you get a smaller and cheaper to run model that can be used by more people.

That is not what PALM E is about though. This is the paper where they patch an LLM so that it can give command to a real, physical robot and basically let the LLM interact with meatspace.