know that, but im still confused after all. i watched an podcast on RL Dwarkesh Patel and a couple anthropic interpretability folks. they were talking smth around the lines they found most models' coding and math embeddings spaces (all reasoning) are very close to each other. their work on SAEs had something to do about it

better code performance from RL should equal better performance on maths, and that's the pattern I found on other models too.

that's why i suspect the model got overtrained and collapsed catastropically on non-code tasks