> "Once these models achieve a high level of comprehension, training larger models with more data may not offer significant improvements (not to be mistaken with reinforcement learning through human feedback). Instead, providing LLMs with real-time, relevant data for interpretation and understanding can make them more valuable."

To me this viewpoint looks totally alien. Imagine you have been training this model to predict the next token. At first it can barely interleave vowels and consonants. Then it can start making words, then whole sentences. Then it starts unlocking every cognitive ability one by one. It begins to pass nearly every human test and certification exam and psychological test of theory of mind.

Now imagine thinking at this point "training larger models with more data may not offer significant improvements" and deciding that's why you stop scaling it. That makes absolutely no sense to me unless 1) you have no imagination or 2) you want to stop because you are scared to make superhuman intelligence or 3) you are lying to throw off competitors or regulators or other people.