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TL;DR
Language models are trained through a three-stage process: initial pre-training on vast text data, post-training to shape behavior, and deployment where models do not learn from interactions. This process explains their capabilities and limitations.
Language models are not single, constantly learning entities but are instead built through a multi-stage process involving pre-training, post-training, and fixed deployment. This process determines their ability to generate accurate answers and explains why they do not learn from individual interactions, according to Thorsten Meyer.
The training pipeline begins with pre-training, which involves processing trillions of tokens of text to develop raw language and knowledge capabilities. This stage lasts months and results in a base model that can generate fluent text but does not follow specific instructions or exhibit behavior traits.
Following pre-training, the post-training phase refines the model’s behavior through instruction tuning, reward modeling, and reinforcement learning. These steps embed guiding principles, improve responsiveness, and align outputs with human preferences, transforming the base model into a practical assistant.
Once deployed, the model’s weights are frozen. It no longer learns or adapts from conversations; each response is generated based solely on the fixed weights, which encode the training outcomes. This explains why models do not improve or change through user interactions.
One map, three timescales. Capability is built once over months; behaviour is set over weeks; and every answer is assembled in seconds from parts that learned nothing new. Three points along the way are where alignment actually lives.
Implications of Fixed Weights for AI Reliability
This training approach clarifies why language models can produce highly fluent responses but do not improve through use. It underscores the importance of the initial training and post-training phases in shaping behavior, affecting how users and developers understand and trust these systems. Recognizing that models do not learn during deployment helps set realistic expectations about their capabilities and limitations.
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Training Phases and Their Role in Model Development
The concept of training language models in multiple stages is well-established in AI research. Pre-training, which lasts months, creates a broad language understanding. Post-training, involving instruction tuning and reinforcement learning, shapes the model's responses to be more aligned with user needs and safety considerations. This process has been refined over recent years to produce models like GPT-4, which are widely used today. However, misconceptions persist about whether models learn from interactions, which this process clarifies.
"The model does not learn from talking to you. It does not remember your last conversation because it 'learned' from it. Its weights are fixed once deployed."
— Thorsten Meyer
Unresolved Questions About Model Adaptability During Deployment
It remains unclear whether future developments might enable models to adapt or learn from interactions without retraining, or if new architectures could allow for ongoing learning in deployment. Currently, models are fixed after training, but research into continual learning is ongoing.
Future Directions in Model Training and Deployment Practices
Researchers are exploring methods to enable models to adapt dynamically during deployment, potentially allowing for ongoing learning or personalization. However, these approaches are still experimental, and the current standard remains fixed weights post-deployment. Expect ongoing developments in AI training techniques and safety measures.
Key Questions
Do language models learn from conversations?
No, once deployed, language models do not learn or remember individual conversations. Their responses are generated based on fixed weights from the training process.
What is the purpose of post-training?
Post-training refines the model's behavior by instruction tuning, reward modeling, and reinforcement learning, making it more helpful, safe, and aligned with human preferences.
Can models be updated after deployment?
Currently, models are fixed after training and do not learn from new interactions. Updating involves retraining or fine-tuning the model with new data in a controlled process.
Why do models sometimes give incorrect answers?
Models generate responses based on patterns learned during training. They can produce inaccuracies if the training data was incomplete or biased, or if the prompt is ambiguous.
Source: ThorstenMeyerAI.com
