Deepseek obviously trained on OpenAI outputs, which were originally RLHF'd. It may seem that we've got all the human feedback necessary to move forward and now we can infinitely distil + generate new synthetic data from higher parameter models.
I’ve seen this claim but I don’t know how it could work. Is it really possible to train a new foundational model using just the outputs (not even weights) of another model? Is there any research describing that process? Maybe that explains the low (claimed) costs.
Those were probably from OpenAI models. Then they used reinforcement learning to expand the reasoning capabilities.