alt Hacker NewsMy understanding is that current LLMs aren't really well suited to do this - tokens are predetermined, and while embeddings are learned, they are learned from an existing corpus of text, which presumably comes from a human language. After this point the language is locked in. There really isn't a kind of training which could efficiently change its embedding representation. I mean, you could probably instruct an LLM to design a more compact language, generate synthethic data and train a new gen on that, but that would be a fairly explicit process and not something that would emerge during training.
> tokens are predetermined, and while embeddings are learned, they are learned from an existing corpus of text, which presumably comes from a human language
That's not true since are least multimodal models - token space is broader now, encompassing visual and audio signals. Tokens are more like sensory/perception units now, not digitized pieces of writing.
I imagine LLMs exhibit this tendency for compressed communication in post-training/RL phase. Particularly with CoT, until interpretability became baked in as grading criteria.