Ask these questions again in two years when the next winter happens.
They trained models on only task specific data, not on a general dataset and certainly not on the enormous datasets frontier models are trained on.
"Our training sets consist of 2.9M sequences (120M tokens) for shortest paths; 31M sequences (1.7B tokens) for noisy shortest paths; and 91M sequences (4.7B tokens) for random walks. We train two types of transformers [38] from scratch using next-token prediction for each dataset: an 89.3M parameter model consisting of 12 layers, 768 hidden dimensions, and 12 heads; and a 1.5B parameter model consisting of 48 layers, 1600 hidden dimensions, and 25 heads."
If you don't believe me, here is a fun mental exercise: define "understand" and "reason" in a measurable way, that includes humans but excludes LLMs.
> The `foobar` is also incorrect. It should be a valid frobozz, but it currently points to `ABC`, which is not a valid frobozz format. It should be something like `ABC`.
Where the two `ABC`s are the exact same string of tokens.
Obviously nonsense to any human, but a valid LLM output for any LLM.
This is just one example. Once you start using LLMs as tools instead of virtual pets you'll find lots more similar.
But a LLM is sometimes the genius and sometimes the idiot.
That doesn’t happen often if you always talk to the same person
But for LLMs these kinds of illogical statements are common.
LLMs don't think; what they do cannot be in good faith called "thinking" by any definition.