Comment by xyzzy123
1 day ago
I'm super confused. The small model "cost field" `rag-api/geometric_lens/cost_field.py` was trained on PASS_TASKS like "Write a function that counts vowels in a string." and FAIL_TASKS like "Write a function that converts a regular expression string to an NFA using Thompson's construction, then converts the NFA to a DFA.".
So it seems like it's a difficulty classifier for task descriptions written in English.
This is then used to score embeddings of Python code, which is a completely different distribution.
Presumably it's going to look at a simple solution, figure out it lands kinda close to simple problems in embedding space and pass it.
But none of this helps you solve harder problems, or distinguish between a simple solution which is wrong, and a more complex solution which is correct.
I think the goal is to have a light heuristic that helps find plausibly useful solutions. They're still going to go through a testing phase as a next step, so this is just a very simple filter to decide what's even worth testing.
> But none of this helps you solve harder problems, or distinguish between a simple solution which is wrong, and a more complex solution which is correct.
It does because hallucinations and low confidence share characteristics in the embedding vector which the small neural learns to recognize. And the fact that it continuously learns based on the feedback loop is pretty slick.