Continual learning isn't a "fundamental limitation" or unsolvable problem. Animal brains are an existence proof that it's possible, but it's tough to do, and quite likely SGD is not the way to do it, so any attempt to retrofit continual learning to LLMs as they exist today is going to be a hack...
Memory and learning are two different things. Memorization is a small subset of learning. Memorizing declarative knowledge and personal/episodic history (cf. LLM context) are certainly needed, but an animal (or AI intern) also needs to be able to learn procedural skills which need to become baked into the weights that are generating behavior.
Fine tuning is also no substitute for incremental learning. You might think of it as addressing somewhat the same goal, but really fine tuning is about specializing a model for a particular use, and if you repeatedly fine tune a model for different specializations (e.g. what I learnt yesterday, vs what I learnt the day before) then you will run into the catastrophic forgetting problem.
I agree that incremental learning seems more like an engineering problem rather than a research one, or at least it should succumb to enough brain power and compute put into solving it, but we're now almost 10 years into the LLM revolution (attention paper in 2017) and it hasn't been solved yet - it's not easy.
Fundamentally, I’m more optimistic on how far current approaches can scale. I see no reason why RL could not be used to train models to use memory, and fine-tuning already works, it’s just expensive.
The continual learning we get may be a bit hamfisted, and not fit into a neat architecture, but I think we could actually see it work at scale in the next few years. Whereas new techniques like what Yann Lecun have demonstrated still live heavily in the realm of research. Cool, but not useful yet.
Fine tuning is also not so limited as you suggest. For one, we don’t need to fine tune the same model over and over, you can just start with a frontier model each time. And two, modern models are much better at generating synthetic data or environments for RL. This could definitely work, but it might require a lot of work in data collection and curation, and the ROI is not clear. But if large companies continue to allocate more and more resources to AI in the next few years, I could see this happening.
OpenAI already has a custom model service, and labs have stated they already have custom models built for the military (although how custom those models are is unclear). It doesn’t seem like a huge leap to also fine-tune models over a companies internal codebases and tooling. Especially for large companies like Google, Amazon, or Stripe that employ tens of thousands of software engineers.
Memory and learning are two different things. Memorization is a small subset of learning. Memorizing declarative knowledge and personal/episodic history (cf. LLM context) are certainly needed, but an animal (or AI intern) also needs to be able to learn procedural skills which need to become baked into the weights that are generating behavior.
Fine tuning is also no substitute for incremental learning. You might think of it as addressing somewhat the same goal, but really fine tuning is about specializing a model for a particular use, and if you repeatedly fine tune a model for different specializations (e.g. what I learnt yesterday, vs what I learnt the day before) then you will run into the catastrophic forgetting problem.
I agree that incremental learning seems more like an engineering problem rather than a research one, or at least it should succumb to enough brain power and compute put into solving it, but we're now almost 10 years into the LLM revolution (attention paper in 2017) and it hasn't been solved yet - it's not easy.