Yes, but pre-training of any sort is no substitute for being able to learn how to act from your own experience, such as learning on the job.
An LLM is an auto-regressive model - it is trying to predict continuations of training samples purely based on the training samples. It has no idea what were the real-world circumstances of the human who wrote a training sample when they wrote it, or what the real-world consequences were, if any, of them writing it.
For an AI to learn on the job, it would need to learn to predict it's own actions in any specific circumstance (e.g. circumstance = "I'm seeing/experiencing X, and I want to do Y"), based on it's own history of success and failure in similar circumstances... what actions led to a step towards the goal Y? It'd get feedback from the real world, same as we do, and therefore be able to update it's prediction for next time (in effect "that didn't work as expected, so next time I'll try something different", or "cool, that worked, I'll remember that for next time").
Even if a pre-trained LLM/AI did have access to what was in the mind of someone when they wrote a training sample, and what the result of this writing action was, it would not help, since the AI needs to learn how to act based on what is in it's own (ever changing) "mind", which is all it has to go on when selecting an action to take.
The feedback loop is also critical - it's no good just learning what action to take/predict (i.e what actions others took in the training set), unless you also have the feedback loop of what the outcome of that action was, and whether that matches what you predicted to happen. No amount of pre-training can remove the need for continual learning for the AI to correct it's own on-the-job mistakes, and learn from it's own experience.
An LLM is an auto-regressive model - it is trying to predict continuations of training samples purely based on the training samples. It has no idea what were the real-world circumstances of the human who wrote a training sample when they wrote it, or what the real-world consequences were, if any, of them writing it.
For an AI to learn on the job, it would need to learn to predict it's own actions in any specific circumstance (e.g. circumstance = "I'm seeing/experiencing X, and I want to do Y"), based on it's own history of success and failure in similar circumstances... what actions led to a step towards the goal Y? It'd get feedback from the real world, same as we do, and therefore be able to update it's prediction for next time (in effect "that didn't work as expected, so next time I'll try something different", or "cool, that worked, I'll remember that for next time").
Even if a pre-trained LLM/AI did have access to what was in the mind of someone when they wrote a training sample, and what the result of this writing action was, it would not help, since the AI needs to learn how to act based on what is in it's own (ever changing) "mind", which is all it has to go on when selecting an action to take.
The feedback loop is also critical - it's no good just learning what action to take/predict (i.e what actions others took in the training set), unless you also have the feedback loop of what the outcome of that action was, and whether that matches what you predicted to happen. No amount of pre-training can remove the need for continual learning for the AI to correct it's own on-the-job mistakes, and learn from it's own experience.