As long as the model itself understands that when it says "foo bar" in its own reasoning trace it means "pursue theory foo; no, this didn't lead anywhere, let's backtrack and pursue theory bar", where's the loss in clarity?
How could it understand, if there's no distinction between that and "from foo logically follows bar"? If the notes it's taking for itself don't make sense, it might just as well not take them.
Obviously it's going to have to come up with unambiguous shorthand for itself during gradient descent. My point is that that shorthand doesn't necessarily have to be unambiguous to human observers.
I feel you have both missed the point of caveman reasoning.
LLMs are trained on human natural language, not a specialised internal-only monologue to make syntactic shortcuts. Their response should make grammatical sense to a human reader because they are mimicking human speech.
"foo bar" is ambiguous.
"pursue theory foo; no, this didn't lead anywhere, let's backtrack and pursue theory bar" is accurate, should be meaningful to LLM attention, but is too verbose.
The minimal caveman way to say this is "not foo. instead do bar".
This really should provide the LLM attention with everything it needs to grasp the intention, but with far fewer tokens used.
