> The most promising idea is to use reasoning models to generate data, and then train our non-reasoning models with the reasoning-embedded data.
Why is it promising, aren’t you potentially amplifying AI biases and errors?
Why is it promising, aren’t you potentially amplifying AI biases and errors?
https://newsletter.languagemodels.co/i/155812052/large-scale...
also from the posted article
"""
The R1-Zero training process is capable of creating its own internal domain specific language (“DSL”) in token space via RL optimization.
This makes intuitive sense, as language itself is effectively a reasoning DSL.
"""