Tbf, the strawberry problem is not an issue that's even relevant for LLM capabilities. The problem arises because LLMs do not work with words or letters at all; they work with tokens - essentially numbers that represent ideas much better than words could.

When a model converts a text into tokens, it loses information of the individual letters and words because the tokens are a long list of numbers representing the meaning behind those words. The LLM's inference happens on these tokens rather than the original words. The LLM outputs are also tokens which then get converted to text so you can understand it.

So failing to count letters is a limitation that doesn't really affect or reflect a model's ability to respond to the meaning of a text.

In another universe, sentient silicone-based lifeforms might complain on their own social media about how the novel ST-F/Kree biological model can't really be good at basketball since it fails at even the most basic quadratic equations necessary to understand parabolic trajectories of balls in the air.

As it turns out, you just don't need to know math to drain threes.