In my opinion, that linked article is misleading. At its core, an LLM is still primarily trained to predict tokens in a sequence, and the instruction fine-tuning is about tweaking the format of what types of sequence it’s modeling (so that it’s predicting what comes next specifically in the context of a sequence that has alternating user prompts and system responses). This change in format doesn’t, in and of itself, imply improved “understanding” or “reasoning” not already within the capability of the base model.
The premise of using a language model as general-purpose AI has always been that modeling text requires “understanding” in some sense, and earlier models like GPT-2 that were not fine-tuned for instruction following could still be evaluated on things like question-answering benchmarks. They just did this by asking them to predict the next token in contexts that were structured such that the next token would plausibly contain the answer being sought. The examples of GPT-2 failing to respond to direct questions are highly misleading, because that’s not the appropriate way to solicit information from a model that hasn’t been fine-tuned for question answering. If you want to see if a model not fine-tuned for instruction following “knows” who the president was in 1880, you prompt it with something along the lines of "The name of the president of the United States in 1880 was ", or possibly even "Q: Who was the president of the United States in 1880? A: "
Obviously you can argue at length about the meaning of “reasoning” and the exact relationship between autoregressive language modeling and modern agents or whatever, but it continues to be true that the underlying model driving everything is built on a core of statistical modeling of sequences of tokens. This is not “false” or “misleading,” and while you could argue that it’s reductive or fails to take certain modern developments into account, the linked article misleadingly (IMO) attributes way more significance to superficial formatting differences than is called for.