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TL;DR
This article explains the three stages of AI model development: pre-training, post-training, and inference. It clarifies how models learn, are shaped, and do not learn from conversations after deployment, highlighting their impact and limitations.
The training of AI language models involves three distinct stages: pre-training, post-training, and inference, each with specific roles in shaping the model’s capabilities and behavior. Once deployed, the model’s weights are fixed, meaning it does not learn from ongoing conversations. This process is critical for understanding how these models generate answers and why their behavior remains consistent over time, impacting their reliability and limitations.
Pre-training, which takes months, involves exposing the model to trillions of text tokens to develop raw language and knowledge capabilities. This stage uses a simple objective: predict the next token in a sequence, resulting in a fluent but behaviorally neutral base model that does not follow instructions or decline prompts.
Post-training, occurring over weeks, transforms the base model into a helpful assistant. It involves four key steps: defining a model specification (or constitution), instruction tuning with curated example responses, training a reward model to evaluate answer quality, and reinforcement learning to align the model’s responses with desired behaviors. This stage embeds values and behavior patterns into the model’s weights.
Once deployed, the model’s weights are frozen and do not change. It responds to user prompts based on its fixed training, without learning from individual interactions. Any perceived memory or continuity across conversations is simulated, not learned, and does not influence future responses.
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 in AI Deployment
Understanding that AI models do not learn from ongoing conversations clarifies their limitations and reliability. It explains why models behave consistently and why they cannot adapt or improve based on individual user interactions without retraining. This knowledge is essential for developers, users, and policymakers to set appropriate expectations and ensure responsible use of AI systems.
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Stages of AI Model Development and Deployment
The development of large language models involves a lengthy pre-training phase where models learn general language patterns from vast datasets. This is followed by post-training, where models are fine-tuned to align with specific behaviors and values through instruction tuning, reward modeling, and reinforcement learning. Once these steps are complete, the models are deployed with fixed weights, meaning they do not continue learning or adapting in real-time. This process has been clarified through recent industry disclosures and research, emphasizing the separation of training and inference phases."The model that answers your thousandth message is byte-for-byte identical to the one that answered your first."
— Thorsten Meyer
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Uncertainties About Model Adaptation and Updates
It remains unclear how future advancements might enable models to update or learn post-deployment without full retraining. Some research explores online learning or continual training methods, but these are not yet standard in deployed systems, and their implications are still being studied.
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Future Developments in AI Training and Deployment
Research is ongoing into methods that could allow models to adapt in real-time or learn incrementally after deployment. However, current systems will likely continue to rely on fixed weights, with updates requiring retraining. Developers and organizations will need to balance model stability, safety, and adaptability in future AI systems.
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Key Questions
Do AI models learn from conversations with users?
No, once deployed, AI models do not learn or remember individual conversations. They respond based on their fixed training data and weights.
Can a model's behavior change over time?
Only if the model is retrained or updated by its developers. The deployed model itself remains static between updates.
Why do AI models seem to remember past interactions?
They simulate memory by including previous conversation context in prompts, but they do not actually learn or store information from these interactions.
What are the main stages in training an AI language model?
The three stages are pre-training (building raw capability), post-training (shaping behavior and values), and inference (responding to users with fixed weights).
Will future AI systems be able to learn continuously?
This is an active area of research. While some approaches aim for online learning, most current deployed models will remain static until explicitly retrained.
Source: ThorstenMeyerAI.com
