1.0 is "natural units". If your energy corresponds to nats, you should be using temperature 1.0. If your energy corresponds to bits, you should be using temperature ln(2) ~= 0.7. The optimization pressure is

     max nats = max entropy + energy / temperature

Why might energy correspond to bits or nats? Imagine your goal is to play as many interesting games of chess as possible in a tournament. This implies you have to keep winning. If you look at the RL environment from the right perspective, you can turn it into optimizing bits or nats.

If RL was used to train the model, the model will have been trained on its own sequences. Those will have been generated with a temperature of 1.0. They must be, otherwise you would get a premature collapse or explosion of your entropy if the temperature was respectively lower or higher.

After that RL step, you want to stick to the RL distribution, and so keep a temperature of 1.0. Other temperatures will drive the model out-of-distribution.

That is why the sampling step for agents or thinking LLMs are usually kept at a temperature of 1.0.

It really depends on the application does it not? I'm not an LLM guy, but for creative tasks like storytelling wouldn't you want a higher temperature usually? Happy to gain insight from anyone with experience here :)

Heavily depends on the model architecture and the implementation though, I don't think you can say what values are better than others without first specifying those, otherwise it's straight up guessing, ironically.

If you use a model in a configuration far from where it was RLed you get no warranty. (you also get no warranty the other way, however)

temperature is the wrong tool

the variance is caused by the bad evaluation prompt

if you ask "what is the capital of Paris" you'll always get Paris, with any (non-extreme) temperature