Many people here are right, compute, collapse, forgetting whatever.
The only "real" way to do this would be: 1. Train a model 2. New data 3. Retrain the model in full + new data 4. Repeat 5. You still have no garuntee on the "time" aspect though.
But CL as a field basically has zero answers on how to do this in a true sense. It's crazy hard because the "solutions" are hypocritical in many ways.
We need to expand the model's representation space while keeping the previous representation space nearly the same?
Basically, you need to modify it without changing it.
Most annoying is that even the smallest of natural brains do this easily. I have a long winded theory but basically it boils down to AI likely needs to "sleep" or rest somehow.
But the clone couldn't run without sleeping? So that's more of a teammate than a clone.
1 works while the other sleeps and then swap.
If this method ever worked our current alignment methods get chucked out the window those would be two completely different AI.
1. Preventing collapse -> model gets "full" https://arxiv.org/pdf/1612.00796
2. Forgetting causes better generalization https://arxiv.org/abs/2307.01163
3. Unknow paper that connects this - allow a "forgetting" model that improves generalization over time. - I tried for a long time to make this but it's a bit difficult
Fun implication is that if true this implies AGI will need "breaks" and likely need to consume non task content of high variety much like a person does.
CL has been an open problem from the very beginnings of AI research with basically no solution. Its pervasiveness indicates a very deep misunderstanding on our knowledge of reasoning.
I just like talking about it. I will say that learning outside distribution content while keeping previous knowledge in a "useful" state is a capability that would absolutely supercharge ever AI method we currently have.
It's atleast an honest atempt at a research direction other than "scale infinitely for everything" that we currently do.
Just think about how natural brains do something incredible.
1. They have fixed computation budgets per time step. 2. They continously learn entirely new tasks while still maintaining previous in a useful state.
That's a capability I would very much like in my AI.
Scaling laws are correct but they are also the reason we are nowhere near replacing humans.
Take a simple job maybe admin work. Every timestep depends on the previous timestep. While not a complex job and an AI could do it for awhile but over time the compuation required to "look back" its memory and connect it for the next step grows near exponentially.
RAG is another perfect example of this problem.
I do deeply belive AGI will be solved by a kid with a whiteboard not a supercluster. CL is my best guess at what that means.
Maybe it's a super RL or energy type method but I've never seen it.
LoRA paper: https://arxiv.org/abs/2106.09685
They try to keep a single continuous "state" that always updates.
It's more about going "farther" than something more akin to "go forever" that CL promises.
Scaling laws are true in that infinite scale would 100% lead to AGI. But at the same time the problem with it is that you can't infinitely scale the computation per task.
RL solves this problem in general but it has a deep assumption of knowing the future. Step too far out of the box and it collapses.
The smallest natural brains handle unknown future states with a fixed computation budget per timestep which is truly incredible.
Online learning is more akin to RL in that it's a structured and boxed enviroment. Step outside of that box or the box changes too much and you collapse.
CL is much more similar to meta learning. The concepts are more about learning NEW content while keeping previous the same.
CL is a completely open problem with all model types. EWC is amoung the better attempts (and a favorite of mine) at solving it with big limitations.
I completely agree that figuring out a safe way to continually train feels like the biggest blocker to AGI
Hypothetically (and perhaps more plausibly), a continually learning model that adapts to the context of a particular org / company / codebase / etc., could even be desirable.
does a model need to retain its generality?
Only if you want it to remain smart.
There are tons of benchmarks around this you can easily run with 1 gpu.
It's compute only in the sense that the only way to do it is retrain a model from scratch at every step.
If you solve CL with a CNN you just created AGI.
But it doesn't solve the time aspect.
You need to randomize data in order to train to best quality. In doing that the model has no idea t0 was before t1000. If you don't you get model collapse or heavy bias.
Some attempts at it but nothing crazy effective.
I’m sure this is something the big labs are trying but from the outside as a user of LLMs it feels like people don’t talk about this very much and instead the focus right now is on better training (eg reinforcement learning) with the assumption that anything else not learned during training will be stuffed into the context somehow as needed. But from a naive perspective the lack of learning from experience after training seems like the biggest thing standing between us and AGI.