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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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The Cost of Training
At the moment, the pre-training stage remains the most expensive part of the overall training process. However, as post-training techniques continue to evolve, it's possible that the balance could shift, with post-training becoming the more costly component. This would likely involve scaling up methods that rely on human interaction, such as debate or iterated amplification, rather than direct human feedback.
Constitutional AI: Aligning Models with Principles
The concept of "Constitutional AI," as described in a 2020 paper, introduces the idea of embedding a set of principles or a "constitution" into the model's decision-making process. This allows the AI system to evaluate its own responses against these principles, effectively engaging in a form of self-play to improve its alignment with the specified criteria.
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The key challenge in this approach is determining who defines the constitution and the underlying principles. In practice, this could vary depending on the intended use case and customer, with the possibility of specialized or customized principles for different applications. The goal is often to create more neutral and advisory AI systems that present considerations rather than express strong opinions.
Overall, the evolution of AI training techniques reflects a continuous effort to enhance the capabilities, safety, and alignment of these systems with human values and preferences. While pre-training remains a crucial component, the post-training phase is becoming increasingly important, with techniques like Constitutional AI offering promising avenues for further development.