My take on the reasoning data limitation in Rafael's Meta-CoT paper:

Can we address this by generating incremental reasoning challenges and resetting the system to "zero" to simulate learning from scratch?

Instead of presenting advanced models with fully-formed problems, we could:

Break down complex problems (like the Pythagorean theorem) into constituent axioms and primitives.
Design tasks that guide models through progressively complex reasoning steps, starting from foundational truths and building up.
For example:
- Start with Euclidean geometry axioms (e.g., triangle properties, congruence rules).
- Introduce simpler derived results (e.g., sum of angles in a triangle).
- Gradually challenge the model to "rediscover" the Pythagorean theorem by synthesizing these building blocks. 
This approach fosters true reasoning skills instead of merely recalling solutions from pre-training.
What do you think? @rm_rafailov