the distillation everyone talks about in respect to LLM's isn't nearly as easy as most think.

none of the frontier labs provide probability distributions over the tokens which is the actual method of distillation you use to train a smaller model based on a larger one. they don't even provide all the tokens.

therefore this so-called distillation the frontier labs whine about is just a set of clever methods to work the existing LLM into the training process for a new model. methods like having the existing model grade the output of the new model and work those grades into the RL method. give the new models structured tasks and use the existing model as a source of truth for those tasks and a myriad of other hacks.

efficiency scales with the gap between the models and generally allows an efficient bootstrap process. the implication that distillation wouldn't allow further advancement is false however, you can then start doing the same thing the frontier labs have been doing: dumping cash on humans to provide the signals or burning tokens on exploratory paths and grading the results.

what openai and anthropic don't like is that fact that all the cash they burned can be used to benefit everyone and not just them. and that no matter how much more cash they burn to build up the gap it will closed at a small fraction of the price.