printing a bunch of whitespace is a way of entering into a new state ( I am thinking about a state machine), so the LLM can use that whitespace as a new token that can be used later to refine the state of the system. In math terms, whitespace is a tag for a class (or state) in the LLM. I think that perhaps RL can take advantage of such tags. For example whitespace could indicate a point of low gradient (indetermination) or a branching point, the LLM in some way would learn to enhance the learning rate parameter, so the message in the head of the LLM is: be ready to learn from RL because in your actual state you need to take a branch from a branching point that can enhance your capabilities. This is similar to tossing a coin or a die. The rule could be: when whitespace do increase the learning rate parameter to escape from zero gradient points. Caveat emptor: This is just an speculation, I don't have any data to support this hypothesis. Also this suggests that whitespace could be a "token that reflects the state of previous layers" and is not contained in the vocabulary used to train the model, so I should say that whitespace is a macro-token or neurotoken. If this hypothesis has some ground then it could also be plausible that whitespace could be an enumerate neural tag in the sense that the length of whitespace reflects or is related to the layer in which the zero gradient or branching point occurs. Finally, my throwaway user need whitespace so I will change the password to a random one to force myself to avoid adding new ideas.