The Sequence Chat: The Transition that Changes Everything. From Pretraining to Post-Training in Foundation Models
One of the most impactful transitions in the generative AI space
The release of GPT-01 marked many important milestones in the generative AI space. The model has sparked a tremendous new phase of innovation in reasoning models which has materialized in the release of models such as DeepSeek’s R1 or Alibaba’s QwQ. The magical reasoning capabilities of these models is powered by an increasing transition from pretraining to post-training computation time. In this essay, we will explore the fundamentals behind that transition highlighting the limitations associated with scaling pretraining and the emerging techniques in post-training. Furthermore, it emphasizes the shift away from traditional reinforcement learning with human feedback (RLHF) towards innovative methodologies that promise to enhance model performance and adaptability.