I can't pretend to understand how LLMs work, but I can be sure that anthropomorphizing their functions is not helpful to an objective debate over their abilities.
Does a motor vehicle get "sleep" when it is serviced? When I reboot a computer, is that equivalent to a nap?
They provide an explanation for using the term "sleep":
> In animals, the transfer from short-term memory to long-term memory is thought to be
supported by hippocampal replay [33], especially during sleep [41]; in this phase, short-term hippocampal memories are reactivated and consolidated into cortical synaptic weights. Sleep makes animals unable to respond to external stimuli, suggesting that it must provide enough cognitive benefit to justify this cost [41]. Inspired by these biological processes, we propose a method for transferring context-window memory into persistent weights. When the model’s context window becomes full during inference, the model enters a “sleep” in which it performs multiple forward passes over the accumulated context and recursively updates its fast weights via a learned local rule. As in animal sleep, the model receives no external input tokens during this phase. After consolidation, the context window is cleared, and the model resumes operation with updated fast weights. During training, the model is optimized end-to-end by backpropagating through the entire process to maximize task performance after sleep.
The function of sleep in animals is largely obscure.
One thing we do know for certain is that it is necessary, it is needed in "dumb" animals as well as in you and I. If an animal can't sleep it will eventually die.
I don't think that applies to the activity described in the OP. Does their LLM "die" if it can't perform the function described?
> If an animal can't sleep it will eventually die.
That turns out to be un-settled science. No human has ever died from lack of sleep.
People point to “fatal familial insomnia” as a counterexample. But they die to the disease, not the lack of sleep.
In a series of controlled experiments, rats and fruit flies did die from lack of sleep. But no one has yet proven that it holds true for vertebrates except for rats.
In other words, it could be true that “among vertebrates, only rats die of sleep deprivation.”
So “if an animal can’t sleep, it will eventually die” is actually quite hard to prove, and depending on how you look at it, somewhat easy to disprove by the fact that rats and fruit flies were so difficult to kill from sleep depravation alone.
Personally I’m skeptical of the rat study too. Claude amends this:
> What they did not establish: the mechanism. On autopsy, “no anatomical cause of death was identified.” The rats showed weight loss despite eating more, body temperature problems, and skin lesions, but nothing that pointed to a clean cause. So no, they could not say a rat “died from sleep deprivation alone” in the sense of identifying what sleep loss did to the body to kill it. They showed a strong association under tight controls, not a proven causal pathway.
As far as I understand things, there is a disease which affects your ability to sleep. Once you have it, you suffer chronic insomnia and within 6-18 months you will die. Scientists are uncertain if the cause of the disease causes death or if it's the lack of the capability to sleep; though the consensus is that it's the lack of sleep that is excruciating and the current belief is that it's the lack of sleep that causes the dying. Hard to study an active brain though.
The longest anyone has stayed awake (under controlled conditions) was 11 days, which was met with severe cognitive impairment, paranoia and hullucinations.
I wouldn't assert what you're saying as if it's total fact.
HIV doesn't kill you, but it creates circumstances where other things will. Sleep is the same. You may not die from lack of sleep, but you die from the things it can cause. Effectively there's no difference.
Scientifically you do, otherwise you can’t claim that lack of sleep was the cause of death. It could be an artifact of how the experiment was run, or any number of other factors.
It’s not a small quibble to point out that the central argument (“animals need sleep or they’ll die”) may be mistaken.
> If an animal can't sleep it will eventually die.
Very few animals fail to eventually die even with as much sleep as they want.
But before death, there is a loss of cognitive function from sleep deprivation, and we observe this too with AI whose context windows get too full.
While we don't know very much about sleep, my understanding is that we do have a long list of things that we do during it, we just don't really understand if sleep is necessary for each of them or simply a convenient opportunity for it.
There's lots of things biology does in response to easy-to-detect proxy signals instead of the real thing they care about: Our sensation of needing to breathe more is based on too much carbonic acid in our blood, not lack of oxygen, which is why in general nobody is allowed in an elevator with a liquid nitrogen dewar; Our natural distaste for incest is based on who we grew up with, not our actual DNA; Get too cold and some people suddenly feel warm and want to (and some do) take all their clothes off even though that would just make them hypothermic even faster.
Being asleep may trigger the things we need to get done, but that doesn't mean sleep is *fundamentally* necessary for the things we need to get done. It could be just that it happens to be the way our biochemistry is wired, and we may find some other way to trigger those things.
The quotation given by djeastm would by my guess for what a dream is, and why we have them. But we don't spend all our time asleep, dreaming. And I'd be the first to say that my guess isn't worth much, as I'm not a brain scientist.
I don't think it's necessarily correct to think of sleep in terms of "it is necessary for animals or they will die". It might be more useful to think of it as "it was so useful that animals who slept outcompeted all the animals who didn't".
Meaning: it might just provide a big advantage.
I don't want to overextend and assume that any advantage extends to LLMs. That rest-and-recuperate advantage might also extend to LLM-based AIs. Or maybe not, and the rest-and-recuperate is mainly useful for biology-based organisms. But there is some logic to it.
> The function of sleep in animals is largely obscure.
In my understanding, it's well-understood that sleep is used to consolidate and store long-term memories (amongst other functions, like cell and muscle repair). They've found this memory-consolidation-during-sleep even in relatively simple animals like bees.
> I don't think it's necessarily correct to think of sleep in terms of "it is necessary for animals or they will die". It might be more useful to think of it as "it was so useful that animals who slept outcompeted all the animals who didn't".
You're talking about different things: biological necessity and evolutionary benefit.
You can find out about the former by preventing an animal from sleeping (but otherwise provide all other needed things), and seeing if it will eventually die.
> You can find out about the former by preventing an animal from sleeping (but otherwise provide all other needed things), and seeing if it will eventually die.
That is actually almost impossible to do. The rat study was as close as we’ve ever come, and it’s still debated whether the rats died due to lack of sleep or some other mechanism, since the autopsy couldn’t confirm a cause of death. (It could have been due to the way the experiment ran, for example, not the lack of sleep.)
There is a lot that is known about sleep. We don't know everything and there are large gaps in our knowledge, but there is also a lot that we do know. And this research explicitly tried to emulate the things we know that sleep does do. Calling it "sleep" is warranted, imho.
> The function of sleep in animals is largely obscure.
Also, there's different kinds/stages of sleep, which probably perform different functions.
For instance, REM may do something like the GP describes, consolidating memories and processing learning. Deep sleep may do something else (I vaguely recall some stage of sleep is used by neurons to clear certain waste products).
> Does their LLM "die" if it can't perform the function described?
If you don't periodically clean the context, an LLM effectively goes insane in terms of outputs.
If the LLM were fully controlling a physical system (like a robot body) that contained it the resulting insanity of an ever-growing, never cleaned context would likely result in some sort of death-like event.
This is why I object to sleep() from unistd.h. What an anthropomorphizing notion. Didn't early unix programmers understand that a computer isn't a living creature and therefore isn't capable of sleep? They must have been really stupid!
Anthropomorphization is not inherently wrong, and in some instances, it actually lets you reason better about about complex behavior than whatever convoluted (and often wrong, especially in the case of giant neural networks) mechanistic description one might conjure.
This is the struggle of naming papers. You could stretch definitions and make your own sexy headline or you could be precise and fewer people will read it.
Very much agree that while it is is useful in description of motivation and inspiration,
it is very non-helpful—or worse—to use this language, this way.
One might as well say "need neural plasticity" which is as much an analogy and equally misleading and counterproductive in shaping the right model of the system.
One might even call this pernicious, what it encourages is already a social problem; and it doesn't aid understanding, it confounds it.
The analogy is helpful, but yes we should be able to “intelligently design” something better than sleep analogues since we’re not constrained by evolution like in humans.
Evolution constrains the evolution of human beings, but it's also excellent at discovering elegant designs that work very reliably at a low cost.
Maybe someday we'll understand the way our minds work well enough to design from first principles but until then we've only got one template for how a thinking machine should look.
We are however constrained by the complexity of any purported solution. That's the bitter lesson, in a nutshell.
At the very least, we know that sleep and dreaming do exist in biological brains. (Doesn't mean any of it is applicable to artificial neural nets, doesn't mean it'll work for our specific architectures etc. etc., but at least the idea requires fewer assumptions than a completely untested novel theory.)
>we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache
There is a strong, non-trivial connection here between what your brain does in sleep and what they are studying.
You wouldn't object to referring to robot eyes or robot legs.
The idea of periodically stopping to write blocks of recent context into a fast-weight state is interesting, but I think it liked it better when E2E-TTT[1] did it. It's a more flexible and elegant continuous learning approach.
Essentially it goes "You know how your model can remember its training data? Well, what if you treated its recent context like more training data and updated (some of) the weights using (mostly) the same process used to train it?"
The end result is very good at remembering things but also really good at adapting to new unseen distributions.
That's an idea I had a few months ago: after going through a compaction once the KV cache is nearing capacity, accumulate this knowledge into a dataset to fine-tune a LoRA during offline hours.
This would create a three-layer memory system:
- Stable long-term memory (initial base weights)
- Mid-term memory built from the compactions and replay buffers
- Short-term memory (KV cache)
Sleeping would just be a fancy term for consolidating and transferring information from one memory layer to another during offline hours. Maybe that's also what the brain does while sleeping.
Wouldn't that just accelerate collapse? How much do you trust the outputs of the llm to provide trustworthy and valuable new information? I mean I understand distillation works. But that's much more structured and thoughtful than my sessions at least.
It's a network of computers with GPUs, so there's no reason it can't sleep at the same time it's awake. Just a continuous "sleeping" process going on in the background, incrementally updating the model. No need for the "thinking" process to be "unconscious" while the "sleeping" process runs. Anthropomorphism confuses everything. There's no such thing as "offline hours" because the Earth is a sphere and the United States is not the center of the universe.
Scaling test-time compute has emerged as a key ingredient for enabling large language models (LLMs) to solve difficult problems, but comes with high latency and inference cost. We introduce sleep-time compute, which allows models to "think" offline about contexts before queries are presented: by anticipating what queries users might ask and pre-computing useful quantities, we can significantly reduce the compute requirements at test-time. To demonstrate the efficacy of our method, we create modified versions of two reasoning tasks - Stateful GSM-Symbolic and Stateful AIME. We find that sleep-time compute can reduce the amount of test-time compute needed to achieve the same accuracy by ~ 5x on Stateful GSM-Symbolic and Stateful AIME and that by scaling sleep-time compute we can further increase accuracy by up to 13% on Stateful GSM-Symbolic and 18% on Stateful AIME. Furthermore, we introduce Multi-Query GSM-Symbolic, which extends GSM-Symbolic by including multiple related queries per context. By amortizing sleep-time compute across related queries about the same context using Multi-Query GSM-Symbolic, we can decrease the average cost per query by 2.5x. We then conduct additional analysis to understand when sleep-time compute is most effective, finding the predictability of the user query to be well correlated with the efficacy of sleep-time compute. Finally, we conduct a case-study of applying sleep-time compute to a realistic agentic SWE task.
No, they're actually training weights based on context before compaction. Context is context, this is splitting the model into persistent weights and malleable ones which are periodically updated.
To reach a more brain-like behavior LLMs need to integrate your inputs into their model dynamically, essentially retraining real-time based on the most salient input. Human brains do this selectively all the time and it's part of our plasticity.
Biologically humans do similar compression, so introducing a similar concept to an LLM also feels reasonable. Hardware isn't fast/cheap enough to do this on an ongoing basis, similar to how it's too expensive for our brains to do this while we're moving through the world.
All we have now most of the time in LLMs is "working memory" we're missing a lot of the functionality that allows for episodic memory and selective plasticity.
The more you read about how we think human brains work, the more you can probably realize that we may have figured out a piece with LLMs, but it's certainly nothing approaching AGI. People insisting so are blowing smoke for investor hype or don't understand a big piece.
I can't pretend to understand how LLMs work, but I can be sure that anthropomorphizing their functions is not helpful to an objective debate over their abilities.
Does a motor vehicle get "sleep" when it is serviced? When I reboot a computer, is that equivalent to a nap?
They provide an explanation for using the term "sleep":
> In animals, the transfer from short-term memory to long-term memory is thought to be supported by hippocampal replay [33], especially during sleep [41]; in this phase, short-term hippocampal memories are reactivated and consolidated into cortical synaptic weights. Sleep makes animals unable to respond to external stimuli, suggesting that it must provide enough cognitive benefit to justify this cost [41]. Inspired by these biological processes, we propose a method for transferring context-window memory into persistent weights. When the model’s context window becomes full during inference, the model enters a “sleep” in which it performs multiple forward passes over the accumulated context and recursively updates its fast weights via a learned local rule. As in animal sleep, the model receives no external input tokens during this phase. After consolidation, the context window is cleared, and the model resumes operation with updated fast weights. During training, the model is optimized end-to-end by backpropagating through the entire process to maximize task performance after sleep.
The function of sleep in animals is largely obscure.
One thing we do know for certain is that it is necessary, it is needed in "dumb" animals as well as in you and I. If an animal can't sleep it will eventually die.
I don't think that applies to the activity described in the OP. Does their LLM "die" if it can't perform the function described?
> If an animal can't sleep it will eventually die.
That turns out to be un-settled science. No human has ever died from lack of sleep.
People point to “fatal familial insomnia” as a counterexample. But they die to the disease, not the lack of sleep.
In a series of controlled experiments, rats and fruit flies did die from lack of sleep. But no one has yet proven that it holds true for vertebrates except for rats.
In other words, it could be true that “among vertebrates, only rats die of sleep deprivation.”
So “if an animal can’t sleep, it will eventually die” is actually quite hard to prove, and depending on how you look at it, somewhat easy to disprove by the fact that rats and fruit flies were so difficult to kill from sleep depravation alone.
Personally I’m skeptical of the rat study too. Claude amends this:
> What they did not establish: the mechanism. On autopsy, “no anatomical cause of death was identified.” The rats showed weight loss despite eating more, body temperature problems, and skin lesions, but nothing that pointed to a clean cause. So no, they could not say a rat “died from sleep deprivation alone” in the sense of identifying what sleep loss did to the body to kill it. They showed a strong association under tight controls, not a proven causal pathway.
> No human has ever died from lack of sleep.
As far as I understand things, there is a disease which affects your ability to sleep. Once you have it, you suffer chronic insomnia and within 6-18 months you will die. Scientists are uncertain if the cause of the disease causes death or if it's the lack of the capability to sleep; though the consensus is that it's the lack of sleep that is excruciating and the current belief is that it's the lack of sleep that causes the dying. Hard to study an active brain though.
The longest anyone has stayed awake (under controlled conditions) was 11 days, which was met with severe cognitive impairment, paranoia and hullucinations.
I wouldn't assert what you're saying as if it's total fact.
HIV doesn't kill you, but it creates circumstances where other things will. Sleep is the same. You may not die from lack of sleep, but you die from the things it can cause. Effectively there's no difference.
So? You don't need a proven causal pathway to state that a glass heads towards the ground every time you brush it off a table.
Scientifically you do, otherwise you can’t claim that lack of sleep was the cause of death. It could be an artifact of how the experiment was run, or any number of other factors.
It’s not a small quibble to point out that the central argument (“animals need sleep or they’ll die”) may be mistaken.
> If an animal can't sleep it will eventually die.
Very few animals fail to eventually die even with as much sleep as they want.
But before death, there is a loss of cognitive function from sleep deprivation, and we observe this too with AI whose context windows get too full.
While we don't know very much about sleep, my understanding is that we do have a long list of things that we do during it, we just don't really understand if sleep is necessary for each of them or simply a convenient opportunity for it.
There's lots of things biology does in response to easy-to-detect proxy signals instead of the real thing they care about: Our sensation of needing to breathe more is based on too much carbonic acid in our blood, not lack of oxygen, which is why in general nobody is allowed in an elevator with a liquid nitrogen dewar; Our natural distaste for incest is based on who we grew up with, not our actual DNA; Get too cold and some people suddenly feel warm and want to (and some do) take all their clothes off even though that would just make them hypothermic even faster.
Being asleep may trigger the things we need to get done, but that doesn't mean sleep is *fundamentally* necessary for the things we need to get done. It could be just that it happens to be the way our biochemistry is wired, and we may find some other way to trigger those things.
The quotation given by djeastm would by my guess for what a dream is, and why we have them. But we don't spend all our time asleep, dreaming. And I'd be the first to say that my guess isn't worth much, as I'm not a brain scientist.
I don't think it's necessarily correct to think of sleep in terms of "it is necessary for animals or they will die". It might be more useful to think of it as "it was so useful that animals who slept outcompeted all the animals who didn't".
Meaning: it might just provide a big advantage.
I don't want to overextend and assume that any advantage extends to LLMs. That rest-and-recuperate advantage might also extend to LLM-based AIs. Or maybe not, and the rest-and-recuperate is mainly useful for biology-based organisms. But there is some logic to it.
> The function of sleep in animals is largely obscure.
In my understanding, it's well-understood that sleep is used to consolidate and store long-term memories (amongst other functions, like cell and muscle repair). They've found this memory-consolidation-during-sleep even in relatively simple animals like bees.
> I don't think it's necessarily correct to think of sleep in terms of "it is necessary for animals or they will die". It might be more useful to think of it as "it was so useful that animals who slept outcompeted all the animals who didn't".
You're talking about different things: biological necessity and evolutionary benefit.
You can find out about the former by preventing an animal from sleeping (but otherwise provide all other needed things), and seeing if it will eventually die.
> You can find out about the former by preventing an animal from sleeping (but otherwise provide all other needed things), and seeing if it will eventually die.
That is actually almost impossible to do. The rat study was as close as we’ve ever come, and it’s still debated whether the rats died due to lack of sleep or some other mechanism, since the autopsy couldn’t confirm a cause of death. (It could have been due to the way the experiment ran, for example, not the lack of sleep.)
There is a lot that is known about sleep. We don't know everything and there are large gaps in our knowledge, but there is also a lot that we do know. And this research explicitly tried to emulate the things we know that sleep does do. Calling it "sleep" is warranted, imho.
> The function of sleep in animals is largely obscure.
Also, there's different kinds/stages of sleep, which probably perform different functions.
For instance, REM may do something like the GP describes, consolidating memories and processing learning. Deep sleep may do something else (I vaguely recall some stage of sleep is used by neurons to clear certain waste products).
> Does their LLM "die" if it can't perform the function described?
If you don't periodically clean the context, an LLM effectively goes insane in terms of outputs.
If the LLM were fully controlling a physical system (like a robot body) that contained it the resulting insanity of an ever-growing, never cleaned context would likely result in some sort of death-like event.
but isnt sleep an already defined technical term for significantly reducing power consumption while preserving its state until woken up?
i feel like its confusing to reuse the word for a process that aims to deliberately change state of the machine / process
This is why I object to sleep() from unistd.h. What an anthropomorphizing notion. Didn't early unix programmers understand that a computer isn't a living creature and therefore isn't capable of sleep? They must have been really stupid!
> Does a motor vehicle get "sleep" when it is serviced?
That's more like a doctor visit and a workout. The sleep will be the part of the duty cycle when it's not operating.
> When I reboot a computer, is that equivalent to a nap?
Yes, it wakes up completely refreshed and in good working order, usually, and if there's still a problem you know you need a technician.
Anthropomorphization is not inherently wrong, and in some instances, it actually lets you reason better about about complex behavior than whatever convoluted (and often wrong, especially in the case of giant neural networks) mechanistic description one might conjure.
Here the analogy isn't without reason.
Wason Selection task performance improvements based on social framing suggest that it's easier for us to think about problems when some anthropomorphization is going on. https://www.cep.ucsb.edu/wp-content/uploads/2023/05/Cogadapt...
Is it "Anthropicmorphization" when Claud treats human beings like LLMs?
Interesting question. Is there an actual term for that? It’s like inverse anthropomorphization, but not quite.
This is the struggle of naming papers. You could stretch definitions and make your own sexy headline or you could be precise and fewer people will read it.
If it works, it's called bionics, not anthropomorphization ;)
Just from the title, I’m assuming it refers to a period of downtime used to perform some sort of maintenance on the knowledge held by the system.
Clicking through, that’s exactly what it is. Seems like “sleep” is an excellent term to use here.
I assume compacting is the sleep here; so, yes
Very much agree that while it is is useful in description of motivation and inspiration,
it is very non-helpful—or worse—to use this language, this way.
One might as well say "need neural plasticity" which is as much an analogy and equally misleading and counterproductive in shaping the right model of the system.
One might even call this pernicious, what it encourages is already a social problem; and it doesn't aid understanding, it confounds it.
> When I reboot a computer, is that equivalent to a nap?
I mean, you do put your computer into "sleep" mode and then "wake" it.
Analogies are useful. I think we need to learn how to continue to benefit from them despite the risk of anthropomorphication.
The analogy is helpful, but yes we should be able to “intelligently design” something better than sleep analogues since we’re not constrained by evolution like in humans.
Evolution constrains the evolution of human beings, but it's also excellent at discovering elegant designs that work very reliably at a low cost.
Maybe someday we'll understand the way our minds work well enough to design from first principles but until then we've only got one template for how a thinking machine should look.
We are however constrained by the complexity of any purported solution. That's the bitter lesson, in a nutshell.
At the very least, we know that sleep and dreaming do exist in biological brains. (Doesn't mean any of it is applicable to artificial neural nets, doesn't mean it'll work for our specific architectures etc. etc., but at least the idea requires fewer assumptions than a completely untested novel theory.)
See also, perhaps: https://news.ycombinator.com/item?id=48273597
>we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache
There is a strong, non-trivial connection here between what your brain does in sleep and what they are studying.
You wouldn't object to referring to robot eyes or robot legs.
... and anyway, maybe it was hungry? Or getting the sniffles?
If "reboot" is on the good side of your position, you might have lost the plot.
The idea of periodically stopping to write blocks of recent context into a fast-weight state is interesting, but I think it liked it better when E2E-TTT[1] did it. It's a more flexible and elegant continuous learning approach.
Essentially it goes "You know how your model can remember its training data? Well, what if you treated its recent context like more training data and updated (some of) the weights using (mostly) the same process used to train it?"
The end result is very good at remembering things but also really good at adapting to new unseen distributions.
[1]https://arxiv.org/abs/2512.23675
That's an idea I had a few months ago: after going through a compaction once the KV cache is nearing capacity, accumulate this knowledge into a dataset to fine-tune a LoRA during offline hours.
This would create a three-layer memory system:
- Stable long-term memory (initial base weights)
- Mid-term memory built from the compactions and replay buffers
- Short-term memory (KV cache)
Sleeping would just be a fancy term for consolidating and transferring information from one memory layer to another during offline hours. Maybe that's also what the brain does while sleeping.
Wouldn't that just accelerate collapse? How much do you trust the outputs of the llm to provide trustworthy and valuable new information? I mean I understand distillation works. But that's much more structured and thoughtful than my sessions at least.
We can trust the feedback we give it based on the output it provides.
What kind of feedback are you giving? What's the reward function?
It's a network of computers with GPUs, so there's no reason it can't sleep at the same time it's awake. Just a continuous "sleeping" process going on in the background, incrementally updating the model. No need for the "thinking" process to be "unconscious" while the "sleeping" process runs. Anthropomorphism confuses everything. There's no such thing as "offline hours" because the Earth is a sphere and the United States is not the center of the universe.
related preprint from the letta team https://arxiv.org/abs/2504.13171
Scaling test-time compute has emerged as a key ingredient for enabling large language models (LLMs) to solve difficult problems, but comes with high latency and inference cost. We introduce sleep-time compute, which allows models to "think" offline about contexts before queries are presented: by anticipating what queries users might ask and pre-computing useful quantities, we can significantly reduce the compute requirements at test-time. To demonstrate the efficacy of our method, we create modified versions of two reasoning tasks - Stateful GSM-Symbolic and Stateful AIME. We find that sleep-time compute can reduce the amount of test-time compute needed to achieve the same accuracy by ~ 5x on Stateful GSM-Symbolic and Stateful AIME and that by scaling sleep-time compute we can further increase accuracy by up to 13% on Stateful GSM-Symbolic and 18% on Stateful AIME. Furthermore, we introduce Multi-Query GSM-Symbolic, which extends GSM-Symbolic by including multiple related queries per context. By amortizing sleep-time compute across related queries about the same context using Multi-Query GSM-Symbolic, we can decrease the average cost per query by 2.5x. We then conduct additional analysis to understand when sleep-time compute is most effective, finding the predictability of the user query to be well correlated with the efficacy of sleep-time compute. Finally, we conduct a case-study of applying sleep-time compute to a realistic agentic SWE task.
Kind of related
https://platform.claude.com/docs/en/managed-agents/dreams
Context -> Lora would be soooo cool.
Isn't this simply context pruning/optimization?
From the abstract, it looks like it's actually doing something deeper, updating weights in part of the model?
No, they're actually training weights based on context before compaction. Context is context, this is splitting the model into persistent weights and malleable ones which are periodically updated.
Wouldn’t that be extremely computationaly expensive considering how resource incentive training is?
No, training a state of the art model involves training on the order of 10 trillion tokens.
We're talking about a step that updates weights based on say between 10k and 1M tokens.
I learned something. Thank you!
To reach a more brain-like behavior LLMs need to integrate your inputs into their model dynamically, essentially retraining real-time based on the most salient input. Human brains do this selectively all the time and it's part of our plasticity.
Biologically humans do similar compression, so introducing a similar concept to an LLM also feels reasonable. Hardware isn't fast/cheap enough to do this on an ongoing basis, similar to how it's too expensive for our brains to do this while we're moving through the world.
All we have now most of the time in LLMs is "working memory" we're missing a lot of the functionality that allows for episodic memory and selective plasticity.
The more you read about how we think human brains work, the more you can probably realize that we may have figured out a piece with LLMs, but it's certainly nothing approaching AGI. People insisting so are blowing smoke for investor hype or don't understand a big piece.