I heard that even just getting the model to print a bunch of whitespace ("think for longer") improves the quality of the final response, because some kind of processing is still happening internally?

Could it be that model just uses latent space for thinking while generating almost garbage? Interesting to check if adding repeating something at the end of prompt helps. I.e. model uses it for 'thinking'.

Latent space has a certain shape, which may mean I'm missing a technical distinction.*

There's been publications with a pause token (https://arxiv.org/abs/2310.02226), backspace token (https://arxiv.org/abs/2306.05426), or a think token (https://arxiv.org/html/2405.08644v1#Ch0.S4), all of them based on the theory that a generic token can sort of act a placeholder for manipulating attention further without further meaningful output.

However, in practice, those approaches haven't been used in the training of a large scale model, i.e. I haven't seen it at all, the most adventurous people have gotten at scale is doing Mamba. (and RL)

* It had a particular technical meaning. The first round of the telephone game was when it came to mean "a 3 spatial dimensions-like space, with N dimensions, in an image diffusion model, that contains all possible image styles, that is navigated by a prompt." We're many iterations afield of it now, I'm afraid. Now, you sort of have to interpret it like you would negative space, defined by what it is around it.

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.

Here's one where just appending dots to the output improved quality:

https://arxiv.org/abs/2404.15758

More tokens = fewer options for the final string. It's not any more complicated than that and it doesn't require any reasoning, just an autoregressively trained statistical model of text, but no, it has to be "the model thinks harder if it outputs more tokens".