Here’s a definition of continual learning from an IBM blog post:
Continual learning is an artificial intelligence (AI) learning approach that involves sequentially training a model for new tasks while preserving previously learned tasks. Models incrementally learn from a continuous stream of nonstationary data, and the total number of tasks to be learned is not known in advance.
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance degradation of the old tasks.
The definition of continual learning is not related to generalization, data efficiency, the availability of training data, or the physical limits to LLM scaling.
You could have a continual learning system that is equally data inefficient as current AI systems and is equally poor at generalization. Continual learning does not solve the problem of training data being unavailable. Continual learning does not help you scale up training compute or training data if compute and data are scarce or expensive, nor does the ability to continually learn mean an AI system will automatically get all the performance improvements it would have gotten from continuing scaling trends.
Yes those quotes do refer to the need for a model to develop heterogeneous skills based on private information, and to adapt to changing situations in real life with very little data. I don’t see your problem.
In case it’s helpful, I prompted Claude Sonnet 4.5 with extended thinking to explain three of the key concepts we’re discussing and I thought it gave a pretty good answer, which you can read here. (I archived that answer here, in case that link breaks.)
I gave GPT-5 Thinking almost the same prompt (I had to add some instructions because the first response it gave was way too technical) and it gave an okay answer, which you can read here. (Archive link here.)
I tried to Google for human-written explanations of the similarities and differences first, since that’s obviously preferable. But I couldn’t quickly find one, probably because there’s no particular reason to compare these concepts directly to each other.
No, those definitions quite clearly don’t say anything about data efficiency or generalization, or the other problems I raised.
I think you have misunderstood the concept of continual learning. It doesn’t mean what you seem to think it means. You seem to be confusing the concept of continual learning with some much more expansive concept, such as generality.
If I’m wrong, you should be able to quite easily provide citations that clearly show otherwise.
Here’s a definition of continual learning from an IBM blog post:
Here’s another definition, from an ArXiv pre-print:
The definition of continual learning is not related to generalization, data efficiency, the availability of training data, or the physical limits to LLM scaling.
You could have a continual learning system that is equally data inefficient as current AI systems and is equally poor at generalization. Continual learning does not solve the problem of training data being unavailable. Continual learning does not help you scale up training compute or training data if compute and data are scarce or expensive, nor does the ability to continually learn mean an AI system will automatically get all the performance improvements it would have gotten from continuing scaling trends.
Yes those quotes do refer to the need for a model to develop heterogeneous skills based on private information, and to adapt to changing situations in real life with very little data. I don’t see your problem.
In case it’s helpful, I prompted Claude Sonnet 4.5 with extended thinking to explain three of the key concepts we’re discussing and I thought it gave a pretty good answer, which you can read here. (I archived that answer here, in case that link breaks.)
I gave GPT-5 Thinking almost the same prompt (I had to add some instructions because the first response it gave was way too technical) and it gave an okay answer, which you can read here. (Archive link here.)
I tried to Google for human-written explanations of the similarities and differences first, since that’s obviously preferable. But I couldn’t quickly find one, probably because there’s no particular reason to compare these concepts directly to each other.
No, those definitions quite clearly don’t say anything about data efficiency or generalization, or the other problems I raised.
I think you have misunderstood the concept of continual learning. It doesn’t mean what you seem to think it means. You seem to be confusing the concept of continual learning with some much more expansive concept, such as generality.
If I’m wrong, you should be able to quite easily provide citations that clearly show otherwise.