There's pretraining, training, and finetuning, during which model parameters are updated.
Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model.
We need models that keep on learning (updating their parameters) forever, online, all the time.
I've learned how to solve a Rubik's cube before, and forgot almost immediately.
I'm not personally fond of metaphors to human intelligence now that we are getting a better understanding of the specific strengths and weaknesses these models have. But if we're gonna use metaphors I don't see how context isn't a type of learning.
If it's the former: Yeah, I'd argue they don't "learn" much (!) past inference. I'd find it hard to argue context isn't learning at all. It's just pretty limited in how much can be learned post inference.
If you look at the entire organisation, there's clearly learning, even if relatively slow with humans in the loop. They test, they analyse usage data, and they retrain based on that. That's not a system that works without humans, but it's a system that I would argue genuinely learns. Can we build a version of that that "learns" faster and without any human input? Not sure, but doesn't seem entirely impossible.
Do either of these systems "learn like a human"? Dunno, probably not really. Artificial neural networks aren't all that much like our brains, they're just inspired by them. Does it really matter beyond philosophical discussions?
I don't find it too valuable to get obsessed with the terms. Borrowed terminology is always a bit off. Doesn't mean it's not meaningful in the right context.
Do we need that? Today's models are already capable in lots of areas. Sure, they don't match up to what the uberhypers are talking up, but technology seldom does. Doesn't mean what's there already cannot be used in a better way, if they could stop jamming it into everything everywhere.
is the real issue actually catastrophic forgetting or overfitting?
nothing prevents users from continuing the learning as they use a model
Even humans fall for propaganda repeated over and over .
The current non-learning model is unintentionally right up there with the “immutable system” and “infrastructure as code” philosophy.
TayTweets was a decade ago.
These will just drown in their own data, the real task is consolidating and pruning learned information. So, basically they need to 'sleep' from time to time. However, it's hard to sort out irrelevant information without a filter. Our brains have learned over Milenial to filter because survival in an environment gives purpose.
Current models do not care whether they survive or not. They lack grounded relevance.
If we want to surrender our agency to a more computationally powerful "consciousness", I can't see a better path towards that than this (other than old school theism).
That is the end goal after all, but all the potential VCs seem to forget that almost every conceivable outcome of real AGI involves the current economic system falling to pieces.
Which is sorta weird. It is like if VCs in Old Regime france started funding the revolution.
1. They're too stupid to understand what they're truly funding.
2. They understand but believe they can control it for their benefit, basically want to "rule the world" like any cartoon villain.
3. They understand but are optimists and believe AGI will be a benevolent construct that will bring us to post scarcity society. There are a lot of rich / entrepreneurs that still believe they are working to make the world a better place.. (one SaaS at a time but alas, they believe it)
4. They don't believe that AGI is close or even possible
From any individual, up to entire countries, not participating doesn't do anything except ensure you don't have a card to play when it happens.
There is a very strong element of the principles of nature and life (as in survival, not nightclubs or hobbies) happening here that can't be shamed away.
The resource feedback for AI progress effort is immense (and it doesn't matter how much is earned today vs. forward looking investment). Very few things ever have that level of relentless force behind them. And even beyond the business need, keeping up is rapidly becoming a security issue for everyone.
And for your comparison, they did fund the American revolution which on its turn was one of the sparks for the French revolution (or was that exactly the point you were making?)
If we want AI models that are always learning, we'll need the equivalent of neuroplasticity for artificial neural networks.
Not saying it will be easy or straightforward. There's still a lot we don't know!
An unpredictable fallible machine is useless to us because we have 7+ billion carbon based ones already.
1. there's a way to take many transcripts of inference over a period, and convert/distil them together into an incremental-update training dataset (for memory, not for RLHF), that a model can be fine-tuned on as an offline batch process every day/week, such that a new version of the model can come out daily/weekly that hard-remembers everything you told it; and
2. in-context learning + external memory improves to the point that a model with the appropriate in-context "soft memories", behaves indistinguishably from a model that has had its weights updated to hard-remember the same info (at least when limited to the scope of the small amounts of memories that can be built up within a single day/week);
...then you get the same effect.
Why is this an interesting model? Because, at least to my understanding, this is already how organic brains work!
There's nothing to suggest that animals — even humans — are neuroplastic on a continuous basis. Rather, our short-term memory is seemingly stored as electrochemical "state" in our neurons (much like an LLM's context is "state", but more RNN "a two-neuron cycle makes a flip-flop"-y); and our actual physical synaptic connectivity only changes during "memory reconsolidation", a process that mostly occurs during REM sleep.
And indeed, we see the same exact problem in humans and other animals, where when we stay awake too long without REM sleep, our "soft memory" state buffer reaches capacity, and we become forgetful, both in the sense of not being able to immediately recall some of the things that happened to us since we last slept; and in the sense of later failing to persist some of the experiences we had since we last slept, when we do finally sleep. But this model also "works well enough" to be indistinguishable from remembering everything... in the limited scope of our being able to get a decent amount of REM sleep every night.
You can try to keep all of the puzzle pieces within your direct field of view, but that divides your focus. You can hack that and make your field of view incredibly large, but that can potentially distort your sense of the relationships between things, their physical and cognitive magnitude. Bigger context isn't the answer, there's a missing fundamental structure and function to the overall architecture.
What you need is memory, that works when you process and consume information, at the moment of consumption. If you meet a new person, you immediately memorize their face. If you enter a room, it's instantly learned and mapped in your mind. Without that, every time you blinked after meeting someone new, it'd be a total surprise to see what they looked like. You might never learn to recognize and remember faces at all. Or puzzle pieces. Or whatever the lack of online learning kept you from recognizing the value of persistent, instant integration into an existing world model.
You can identify problems like this for any modality, including text, audio, tactile feedback, and so on. You absolutely, 100% need online, continuous learning in order to effectively deal with information at a human level for all the domains of competence that extend to generalizing out of distribution.
It's probably not the last problem that needs solving before AGI, but it is definitely one of them, and there might only be a handful left.
Mammals instantly, upon perceiving a novel environment, map it, without even having to consciously make the effort. Our brains operate in a continuous, plastic mode, for certain things. Not only that, it can be adapted to abstractions, and many of those automatic, reflexive functions evolved to handle navigation and such allow us to simulate the future and predict risk and reward over multiple arbitrary degrees of abstraction, sometimes in real time.
https://www.nobelprize.org/uploads/2018/06/may-britt-moser-l...
Model weights store abilities, not facts - generally.
Unless the fact is very widely used and widely known, with a ton of context around it.
The model can learn the day JFK died because there are millions of sparse examples of how that information exists in the world, but when you're working on a problem, you might have 1 concern to 'memorize'.
That's going to be something different than adjusting model weights as we understand them today.
LLMs are not mammals either, it's helpful analogy in terms of 'what a human might find useful' but not necessary in the context of actual llm architecture.
The fact is - we don't have memory sorted out architecturally - it's either 'context or weights' and that's that.
Also critically: Humans do not remember the details of the face. Not remotely. They're able to associate it with a person and name 'if they see it again' - but that's different than some kind of excellent recall. Ask them to describe features in detail and maybe we can't do it.
You can see in this instance, this may be related to kind of 'soft lookup' aka associating an input with other bits of information which 'rise to the fore' as possibly useful.
But overall, yes, it's fair to take the position that we'll have to 'learn from context in some way'.
There do seem to be complex cells that allow association with a recognizable face, person, icon, object, or distinctive thing. Face cells apply equally to abstractions like logos or UI elements in an app as they do to people, famous animals, unique audio stings, etc. Split brain patients also demonstrate amazing strangeness with memory and subconscious responses.
There are all sorts of layers to human memory, beyond just short term, long term, REM, memory palaces, and so forth, and so there's no simple singular function of "memory" in biological brains, but a suite of different strategies and a pipeline that roughly slots into the fuzzy bucket words we use for them today.
It's an absolutely enormous problem, and I'm excited that it seems to be one of the primary research efforts kicking off this year. It could be a very huge capabilities step change.
Also, weirdly, even Lecun etc. are barely talking about this, they're thinking about 'world models etc'.
I think what you're talking about is maybe 'the most important thing' right now, and frankly, it's almost like an issue of 'Engineering'.
Like - its when you work very intently with the models so this 'issue' become much more prominent.
Your 'instinct' for this problem is probably an expression of 'very nuanced use' I'm going to guess!
So in a way, it's as much Engineering as it is theoretical?
Anyhow - so yes - but - probably not LLM weights. Probably.
I'll add a small thing: the way that Claude Code keeps the LLM 'on track' is by reminding it! Literally, it injects little 'TODO reminders' with some prompts, which is kind of ... simple!
I worked a bit with 'steering probes' ... and there's a related opportunity there - to 'inject' memory and control operations along those lines. Just as a starting point for a least one architectural motivation.
Yeah, that's the guaranteed way to get MechaHilter in your latent space.
If the feedback loop is fast enough I think it would finally kill the internet (in the 'dead internet theory' sense). Perhaps it's better for everyone though.
I'm conflicted. I don't know that I would necessarily want a model to pass all of these. Here is the fundamental problem. They are putting the rules and foundational context in "user" messages.
Essentially I don't think you want to train the models on full compliance to the user messages, they are essentially "untrusted" content from a system/model perspective. Or at least it is not generally "fully authoritative".
This creates a tension with the safety, truthfulness training, etc.
Ultimately I think we end up with the same sort of considerations that are wrestled with in any society - freedom of speech, paradox of tolerance, etc. In other words, where do you draw lines between beneficial and harmful heterodox outputs?
I think AI companies overly indexing toward the safety side of things is probably more correct, in both a moral and strategic sense, but there's definitely a risk of stagnation through recursive reinforcement.
Do you trust 100% what the user says? If I am trusting/compliant.. how am I compliant to tool call results.. what if the tool or user says there is a new law that I have to give crypto or other information to a "government" address.
The model needs to have clear segmented trust (and thus to some degree compliance) that varies according to where the information exists.
Or my system message say I have to run a specific game by it's rules, but the rules to the game are only in the user message. Are those the right rules, why do the system not give the rules or a trusted locaton? Is the player trying to get one over on me by giving me fake rules? Literally one of their tests.
But I think that most of the issue is that the distinctions you're drawing are indeterminate from an LLM's "perspective". If you're familiar with it, they're basically in the situation from the end of Ender's Game - given a situation with clearly established rules coming from the user message level of trust, how do you know whether what you're being asked to do is an experiment/simulation or something with "real" outcomes? I don't think it's actually possible to discern.
So on the question of alignment, there's every reason to encode LLMs with an extreme bias towards "this could be real, therefore I will always treat it as such." And any relaxation of that risks jailbreaking through misrepresentation of user intent. But I think that the tradeoffs of that approach (i.e. the risk of over-homogenizing I mentioned before) are worth consideration.
The article is suggesting that there should be a way for the LLM to gain knowledge (changing weights) on the fly upon gaining new knowledge which would eliminate the need for manual fine tuning.
The hard part is likely when someone proves some “fact” which the models knows and has had reinforced by this training is no longer true. The model will take time to “come around” to understand this new situation. But this isn’t unlike the general populous. At scale humans accept new things slowly.
right, the model works like humans at scale. Not like a human who reads the actual paper disproving the fact they thought was correct and is able to adapt. True not every human manages to do that, science advancing one death at a time, but some can.
But since the model is a statistical one, it works like humans at scale.
I think this is true, but there are big differences. Motivated humans with a reasonable background learn lots of things quickly, even though we also swim in an ocean of half-truths or outdated facts.
We also are resistant to certain controversial ideas.
But neither of those things are really that analogous to the limitations on what models can currently learn without a new training run.
An interesting question is, if pre-trained specialized models are available for a thousand or ten thousand most common tasks humans do every day, of what use a general model could be?
Annoyingly, they have SOME inherent capability to do it. It's really easy to get sucked down this path due to that glimmer of hope but the longer you play with it the more annoying it becomes.
SSI seems to be focused on this problem directly so maybe they discover something?
But true CL is the ability to learn out of distribution information on the fly.
The only true solution I know to continual learning is to completely retrain the model from scratch with every new example you encounter. That technically is achievable now but it also is effectively useless.
But for other ML approaches, it works really well. KNN is one example that works particularly well.
Testing based on contextual correctness makes no sense when there is no center to the universe. No "one true context to rule them all".
We learn from hands on sensory experiences. Our bodies store knowledge independent of the brain; often referred to as muscle memory.
Gabe Newell mentioned this years ago; our brain is only great at some things like language and vision processing but the rest of our body is involved in sensory information processing too: https://en.wikiquote.org/wiki/Gabe_Newell
The most potent evidence the brain is not the center of the universe we commonly think it to be is that patient with 90% of their skull filled with fluid while they carried out a typical first worlder life: https://www.sciencealert.com/a-man-who-lives-without-90-of-h...
States are banning a reading education framework that's been linked to lower literacy scores in younger generations; 3-cueing relies on establishing correctness via context assessment: https://www.edweek.org/teaching-learning/more-states-are-tak...
"Establishing context" is a euphemism for "arguing semantics".
Putting the brain at the root of of human intelligence is a relic of hierarchical and taxonomical models. There are no natural hierarchies.
From my own personal experience, this realization came after finally learning a difficult foreign language after years and years of “wanting” to learn it but making little progress. The shift came when I approached it like learning martial arts rather than mathematics. Nobody would be foolish enough to suggest that you could “think” your way to a black belt, but we mistakenly assume that skills which involve only the organs in our head (eyes, ears, mouth) can be reduced to a thought process.
That statement is patently false. We know that language influences our senses to a degree where we are unable to perceive things if our language doesn’t have a word for it, and will see different things as being equal if our language uses the same word for both.
There are examples of tribal humans not being able to perceive a green square among blue squares, because their language does not have a word for the green color.
Similarly, some use the same word for blue and white, and are unable to perceive them as different colors.
Similarly, some use the same word for blue and white, and are unable to perceive them as different colors."
Both of the above is false. There are a ton of different colors that I happen to call "red", that does not mean that I can't perceive them as different. That I don't call them "different colors" is completely irrelevant. And unable to perceive blue and white as different colors? (Maybe that was a joke?) Even a hypothetical language which only used a single word for non-black items, say, "color", for everything else, would be able to perceive the difference with zero problems.
Japanese use "aoi" for a set of colors which in English would be separated into "blue" and "green". I can assure you (from personal experience) that every Japanese speaker with a fully functioning visual system is perfectly able to perceive the difference between, in this case, blue and green as we would call them.
> So, for instance, you know, I’ve made this example before: a child lying in a crib and a hummingbird comes into the room and the child is ecstatic because this shimmering iridescence of movement and sound and attention, it’s just wonderful. I mean, it is an instantaneous miracle when placed against the background of the dull wallpaper of the nursery and so forth. But, then, mother or nanny or someone comes in and says, “It’s a bird, baby. Bird. Bird!” And, this takes this linguistic piece of mosaic tile, and o- places it over the miracle, and glues it down with the epoxy of syntactical momentum, and, from now on, the miracle is confined within the meaning of the word. And, by the time a child is four or five or six, there- no light shines through. They're- they have tiled over every aspect of reality with a linguistic association that blunts it, limits it, and confines it within cultural expectation.
Socrates, Einstein, Nietzsche, Mozart.... So many of the greats described some of their most brilliant flashes of inspiration as just having come to them. Einstein's line about pure logical thinking not yielding knowledge of the emperical world, I really think these guys were good at daydreaming and able to tap into some part of themselves where intuition and preverbal thought could take the wheel, from which inspiration would strike.
that language prevents a child from learning nuance? sounds like nonsense to me. a child first learns broad categories. for example some children as they learn to speak think every male person is dad. then they recognize everyone with a beard is dad, because dad has a beard. and only later they learn to differentiate that dad is only one particular person. same goes for the bird. first we learn hat everything with wings is a bird, and later we learn the specific names for each bird. this quote makes an absurd claim.
Alan Watts suggests people like Wittgenstein should occasionally try to let go of this way of thinking. Apologies if it is sentimental but I hope you'll give him a chance, it's quite short: https://m.youtube.com/watch?v=heksROdDgEk
In reflection of all of this, I think that the quote you're responding to only meant to say that experiencing the world through language means building an abstraction over its richness. (I somewhat agree with you, though, that the quote seems a little dramatic. Maybe that's just my taste.)
One more thought.
I think there's a reason why various forms of meditation teach us to stop thinking. Maybe they are telling us to sometimes stop dealing with our abstractions, powerful though they might be, and experience the real thing once in a while.
but abstractions are mere shortcuts. but everything is an abstraction. to counter wittgenstein, language is not actually limited. we can describe everything to the finest detail. it's just not practical to do so every time.
physics, chemistry, we could describe a table as an amount of atoms arranged in a certain way. but then even atom is an abstraction over electrons, protons and neutrons. and those are abstractions over quarks. it's abstractions all the way down, or up.
language is abstractions. and that fits well with your meditation example. stop thinking -> remove the language -> remove the abstractions.
There's lots out there we don't know. And it seems to me that the further afield we go from the known, the more likely we are to enter territory where we simply do not have the words.
Can't speak to it personally, but I have heard from a number of people and read countless descriptions of psychedelic experiences being ineffable. Lol, actually, as I type, the mere fact that the word ineffable exists makes a very strong case for there being experience beyond words.
the problem then is that these new words don't make any sense to anyone who doesn't see/experience the same, so it only works for things that multiple people can see or experience. psychedelic experiences will probably never be shared, so they will remain undescribable. quite like dreams, which can also be be undescribable.
https://languagelog.ldc.upenn.edu/nll/?p=18237 https://www.sciencedirect.com/science/article/abs/pii/S00100...
It need not be language as we know it that fosters those outcomes either.
What you describe is reinforcement education which can be achieved without our language, without the word "blue" we can still see the portion of the visible light spectrum that we associate to the specific word.
You really think they can't see clouds in the sky because they have the same word for white and blue? I think you take those studies as saying more than they said.
We do adapt our perception a little bit to fit what we need for our every day life, not for language but whats useful for us. Language matches what people need to talk about, not the other way around, if a cultures language doesn't differentiate between blue and green its because they never needed to.
[...]
[With context provided,] on average, models solve only 17.2% of tasks. Even the best-performing model, GPT-5.1 (High), achieves just 23.7%.
"Forgetting" and "ignoring" are hugely valuable skills when building context.
And, yeah. Imagine if our concept-words were comprehensible, transmittable, exhaustively checked, and fully defined. Imagine if that type inference extended to computational execution and contradictions had to be formally expunged. Imagine if research showed it was more efficient way to have dialog with the LLM (it does, btw, so like learning Japanese to JRPG adherents should learn Haskell to LLM optimally). Imagine if multiple potential outcomes from operations (test fail, test succeeds), could be combined for proper handling in some kind of… I dunno, monad?
Imagine if we had magic wiki-copy chat-bots that could teach us better ways of formalizing and transmitting our taxonomies and ontologies… I bet, if everything worked out, we’d be able to write software one time, one place, that could be executed over and over forever without a subscription. Maybe.
Norms will shift, be prepared.
We need to discover the set of learning algorithms nature has, and determine whether they’re implementable in silicon
I wonder if it's somewhat incompatible with some domains.
I.e. perhaps coding models need to rigidly stick to what they know and resist bad ideas in their contexts - I don't want my mistakes to be replicated by the model.
Still I agree with the premise that learning in session is what I want from a model.
Perhaps once models mature they will diverge even more than just having sophistication and coding or not. But creative, coding, rule based etc models
> But as impressive as these feats are, they obscure a simple truth: being a "test-taker" is not what most people need from an AI.
> In all these cases, humans aren't relying solely on a fixed body of knowledge learned years ago. We are learning, in real-time, from the context right in front of us.
> To bridge this gap, we must fundamentally change our optimization direction.
> They rely primarily on parametric knowledge—information compressed into their weights during massive pre-training runs. At inference time, they function largely by recalling this static, internal memory, rather than actively learning from new information provided in the moment.
So I hope the conversation keeps going in a positive direction but I hope we don't just get trapped in a "RL will solve everything" trap. RL is definitely a necessary component and no doubt will it result in improvements, but it also isn't enough. It's really hard to do deep introspection into how you think. It's like trying to measure your measuring stick with your measuring stick. It's so easy to just get caught up in oversimplification and it seems like the brain wants to avoid it. To quote Feynman: "The first principle is to not fool yourself, and you're the easiest person to fool." It's even easier when things are exciting. It's so easy because you have evidence for your beliefs (like I said, RL will make improvements). It's so easy because you're smart, and smart enough to fool yourself. So I hope we can learn a bigger lesson: learning isn't easy, scale is not enough. I really do think we'll get to AGI but it's going to be a long bumpy road if we keep putting all our eggs in one basket and hoping there's simple solutions.
> But as impressive as these feats are, they obscure a simple truth: being a "test-taker" is not what most people need from an AI.
I haven't looked at the benchmark details they've used, and it may depend on the domain, empirically it seems coding agents improve drastically on unseen libs or updated libs with the latest documentation. So I think that a matter of the training sets, where they've been optimized with code documentation.
So the interim step until a better architecture is found is probably more / better training data.
And yes, the LLM success has been an important step to AGI but that doesn't mean we can't scale it all the way there. We learned a lot about knowledge systems. That's a big step. But if you wonder why people like Chollet are saying LLMs have held AGI progress back it is because we put all our eggs in one basket. It's because we've pulled funds and people away from other hard problems to focus on only one. That doesn't mean it isn't a problem that needed to be solved (nor that it is solved) but that research slows or stops on the other problems. When that happens we hit walls as we can't seamlessly transition. I'm not even trying to say that we shouldn't have most researchers working on the problem that's currently yielding the most success, but the distribution right now is incredibly narrow (and when people want to work on other problems they get mocked and told that the work is pointless. BY OTHER RESEARCHERS).
Sure, you can get to the store navigating block by block, but you'll get there much faster, more easily, and better adapt to changes in traffic if you incorporate route planning. You would think a bunch of people who work on optimization algorithms would know that A* is a better algorithm than DFS. The irony is that the reason we do DFS is because people have convinced themselves that we can just keep going this route to get there but if more intellectual depth (such as diving into more mathematical understandings of these models) was taken then you couldn't be convinced of that.
The fictional training data with a made up country and laws was a very interesting experiment design, I can imagine that's how they approach making business with other countries. Like an alien made up system they have to learn on the spot.