Part 1/3:

The Evolving Landscape of AI Training

The Role of Pre-Training and Post-Training

The modern AI training process involves a combination of techniques, including supervised fine-tuning, reinforcement learning (RL), and the use of synthetic data. While it can be challenging to precisely measure the relative contributions of pre-training and post-training, the general approach is often more about refining and optimizing the training infrastructure rather than relying on a single "secret sauce."

The success of techniques like reinforcement learning with human feedback (RLhF) lies in their ability to bridge the gap between the model's capabilities and the human's preferences. RLhF doesn't necessarily make the model "smarter" but rather helps it communicate and align better with human needs and desires. This process of "un-hobbling" the model can enhance its helpfulness and responsiveness, even if it doesn't fundamentally alter the model's underlying reasoning abilitie

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