Agreed. I think when it comes to light that Claude has been known to say “I’m DeepSeek” that everyone has had their hand in that cookie jar. Moreover, paying for API calls hardly seems like an attack; ToS violation to be certain but not in the same category of law as criminal activity like hacking.
Didn't Anthropic train on our collective data just to sell it back to us for $100/month? On top of that, Apple is suing them over alleged IP and trade secret theft by ex-Apple employees. Hard to feel too sympathetic, and I’m not an Anthropic hater in particular…
You can’t build a frontier model with one single thing. This is an incremental improvement but it doesn’t explain the entire success of the model. The training set is immensely important, regardless of how you feel about distillation.
I stil don't understand them. I want the US to "win the AI race" but I have trouble understanding how most of all inventions today aren't "distillations" of past knowledge. Is Anthropic claiming the data they stole as trade secrets?
Anthropic is claiming that training an LLM to mimic another LLM is materially different and worse than slurping up stuff written by humans (even if that material is stolen).
Basically, they want IP protection for Claude. This is a nakedly hypocritical stance, but completely understandable from a company-needs-to-make-money standpoint.
Their claim is even stronger than that, they have complaints about their models being used as a validation step for other model output, which is standard practice in the industry.
>To support further research, we open-source the KDA kernel and vLLM implementations, and release the pre-trained and instruction-tuned model checkpoints.
Old but relevant: if you read the recently-released Kimi K3 paper[0], you'll see that it's heavily based on Kimi Linear discussed here, scaling it up and adding a bunch more things (like native vision and RL improvements).
Does any expert in the field know whether it is really the case that this intelligence we are seeing with frontier models is an "emerging" phenomena, only coming up when the architecture is scaled?
Like isn't it weird that the 1 million parameter model with the same architecture can't solve basic puzzles but suddenly the 1 trillion parameter can conjure up counter-examples for the Jacobian conjecture?
It's unintuitive since, to the best of my knowledge, one of the basic tenants of algorithm development was that you can't just brute-force your way towards a solution for some complex problems, e.g. naive sorting algorithms suddenly won't beat quicksort if you put more processing to them, but in the modern LLM scene it seems people are in a race to scaling up, experimenting empirically and hoping the same algorithm/architecture comes to a solution.
This is actually a well-known phenomenon in ML, called "The Bitter Lesson".
> One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.
> one of the basic tenants of algorithm development was that you can't just brute-force your way towards a solution for some complex problems
It's kind of sad that popular CS textbooks often focus on solving precise problems with lowest theoretical complexity bounds while ignoring more practical (but generally applicable) computation techniques.
In machine learning they call it "gradient descent", which in older days had analogies in techniques called "hill climbing", "local search" and "simulated annealing". Basically you have a function you need to optimize for, and you clumsily tweak the parameters so that you get the (locally) max/min value you wanted. These techniques were great at finding approximate, locally maximal solutions without trying all the possibilities at once (which is more akin to the kind of "brute force" in the traditional CS context).
I guess because these techniques were generally applicable yet the outputs were approximate and you couldn't analyze them much (no fancy O(n log n)), the theorists did not find them interesting and thus were not put into the spotlight of student's learning curricula.
In modern machine learning they do this gradient descent thing which is also tweaking the parameters bit by bit to optimize for the loss function, except that the parameters are now in the billions and trillions. The compute required is huge of course, but it's actually quite an "efficient" process, and it's not actually doing much of "brute forcing" at all. During training, the process is essentially, almost equivalent to, compressing the many many trillions of tokens of training data. To me it's quite amazing that they manage to complete such a process within a couple months of training, even if they have hundreds of thousands of GPUs...
It might be that what we consider a basic and very hard puzzle are extremely close together on a more absolute scale. The difference is often for us what proportion of humans can solve it. And the low end of that is still quite high up - animals that can solve things that are very basic for the vast majority of humans are pretty rare and known about, yet are capable of quite complex actions and learning and aren’t wildly different in scale of neurons to us.
Going from 1m to 1T params is also a scaling of a million times. It’s like going from a human brain down to one percent in size in each direction or just a few mm.
IANAMLE, but there is "grokking" that makes models learn to actually generalize, even after you give them enough parameters that would let them memorize the dataset:
High-dimensional gradient descent behaves very differently than the simplified 3d visualisations we use to demonstrate it, and has lots of ways out of local minima:
it's certainly not a definite procedure for determining if an arbitrary mathematical statement is true or not. it's more like educated guess and check which definitely scales up
I started creating internal models using it, then the Gated Deltanet 2 came out( https://arxiv.org/abs/2605.22791), and it seems like an evolution of it in expressiveness. And in our tests it is really better than.
Any one knows how this holds up on long context retrieval (needle in haystack , ruler) vs same size full attention model? efficiency gains look great but that usually where linear attention hybrids fall apart.
The main contribution of the K3 paper is Stable LatentMoE. Like some other models it compresses data sent between layers, which puts certain requirements on the router. K3 improves performance by using a more balanced expert selection strategy.
Compared to the Opus 5 "model card", which read like a standard Anthropic set of alignment principles and safety concerns, this presents a plethora of useful technical details that advances the state of the art.
most new effort in training comes in the late phase with RL techniques
the pretraining (slurping the internet) only goes so far, the new data being used is from human preferences and agent traces (designed and/or distilled)
If you want to believe that the success of Kimi is about distillation attacks, ignore this.
I'd kindly suggest that we could also stop calling them "distillation attacks".
Agreed. I think when it comes to light that Claude has been known to say “I’m DeepSeek” that everyone has had their hand in that cookie jar. Moreover, paying for API calls hardly seems like an attack; ToS violation to be certain but not in the same category of law as criminal activity like hacking.
False dichotomy right?
Are Chinese labs impressively innovating? Clearly.
However this doesn’t rule out possible gains due to distillation.
I don’t know the degree of the latter but both things could certainly be true.
Didn't Anthropic train on our collective data just to sell it back to us for $100/month? On top of that, Apple is suing them over alleged IP and trade secret theft by ex-Apple employees. Hard to feel too sympathetic, and I’m not an Anthropic hater in particular…
That Apple lawsuit is against OpenAi, just for clarity
“Distillation” is just indirectly pirating the largely pirated training data used to train the original model.
“You stole my warez!”
If we do it, it's training a model. When they do it, it's distillation attack. - Anthropic
"You are distilling what I have rightfully pirated."
You can’t build a frontier model with one single thing. This is an incremental improvement but it doesn’t explain the entire success of the model. The training set is immensely important, regardless of how you feel about distillation.
I stil don't understand them. I want the US to "win the AI race" but I have trouble understanding how most of all inventions today aren't "distillations" of past knowledge. Is Anthropic claiming the data they stole as trade secrets?
Anthropic is claiming that training an LLM to mimic another LLM is materially different and worse than slurping up stuff written by humans (even if that material is stolen).
Basically, they want IP protection for Claude. This is a nakedly hypocritical stance, but completely understandable from a company-needs-to-make-money standpoint.
Their claim is even stronger than that, they have complaints about their models being used as a validation step for other model output, which is standard practice in the industry.
Google is apparently taking a different stance and offering distillation as a paid product
https://docs.cloud.google.com/gemini-enterprise-agent-platfo...
But nobody wants to distill Google's models, Gemini is really bad.
The distillation complaints to me sound like when a casino complains about card counting
Does one have to exclude the other?
Well said.
The distillation theory does not even make sense as Fable was only around for days (effectively) before Kimi was released.
>To support further research, we open-source the KDA kernel and vLLM implementations, and release the pre-trained and instruction-tuned model checkpoints.
This is just awesome.
Old but relevant: if you read the recently-released Kimi K3 paper[0], you'll see that it's heavily based on Kimi Linear discussed here, scaling it up and adding a bunch more things (like native vision and RL improvements).
[0] https://arxiv.org/abs/2607.24653
Does any expert in the field know whether it is really the case that this intelligence we are seeing with frontier models is an "emerging" phenomena, only coming up when the architecture is scaled?
Like isn't it weird that the 1 million parameter model with the same architecture can't solve basic puzzles but suddenly the 1 trillion parameter can conjure up counter-examples for the Jacobian conjecture?
It's unintuitive since, to the best of my knowledge, one of the basic tenants of algorithm development was that you can't just brute-force your way towards a solution for some complex problems, e.g. naive sorting algorithms suddenly won't beat quicksort if you put more processing to them, but in the modern LLM scene it seems people are in a race to scaling up, experimenting empirically and hoping the same algorithm/architecture comes to a solution.
This is actually a well-known phenomenon in ML, called "The Bitter Lesson".
> One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.
The full essay is worth a read, it's pretty short http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Is that page served from a secure domain anywhere?
Not quite. The Wikipedia page is worth a look through if you don’t want to click on an http page. https://en.wikipedia.org/wiki/Bitter_lesson
You could read it through internet archive if it's this important.
> one of the basic tenants of algorithm development was that you can't just brute-force your way towards a solution for some complex problems
It's kind of sad that popular CS textbooks often focus on solving precise problems with lowest theoretical complexity bounds while ignoring more practical (but generally applicable) computation techniques.
In machine learning they call it "gradient descent", which in older days had analogies in techniques called "hill climbing", "local search" and "simulated annealing". Basically you have a function you need to optimize for, and you clumsily tweak the parameters so that you get the (locally) max/min value you wanted. These techniques were great at finding approximate, locally maximal solutions without trying all the possibilities at once (which is more akin to the kind of "brute force" in the traditional CS context).
I guess because these techniques were generally applicable yet the outputs were approximate and you couldn't analyze them much (no fancy O(n log n)), the theorists did not find them interesting and thus were not put into the spotlight of student's learning curricula.
In modern machine learning they do this gradient descent thing which is also tweaking the parameters bit by bit to optimize for the loss function, except that the parameters are now in the billions and trillions. The compute required is huge of course, but it's actually quite an "efficient" process, and it's not actually doing much of "brute forcing" at all. During training, the process is essentially, almost equivalent to, compressing the many many trillions of tokens of training data. To me it's quite amazing that they manage to complete such a process within a couple months of training, even if they have hundreds of thousands of GPUs...
It might be that what we consider a basic and very hard puzzle are extremely close together on a more absolute scale. The difference is often for us what proportion of humans can solve it. And the low end of that is still quite high up - animals that can solve things that are very basic for the vast majority of humans are pretty rare and known about, yet are capable of quite complex actions and learning and aren’t wildly different in scale of neurons to us.
Going from 1m to 1T params is also a scaling of a million times. It’s like going from a human brain down to one percent in size in each direction or just a few mm.
IANAMLE, but there is "grokking" that makes models learn to actually generalize, even after you give them enough parameters that would let them memorize the dataset:
https://en.wikipedia.org/wiki/Grokking_(machine_learning)
High-dimensional gradient descent behaves very differently than the simplified 3d visualisations we use to demonstrate it, and has lots of ways out of local minima:
https://www.youtube.com/watch?v=NrO20Jb-hy0
so it seems like there is a benefit to giving models more space to learn in rather than forcing them to compress the knowledge from the start.
Here's one way it could happen:
Let's say there's some circuit that does problem solving of the kind we call intelligence.
We dont know what this circuit looks like, but it exists in our brain.
Doing regression on outputs from the brain (e.g. internet text) with enough parameters, we can "fit" our model to this circuit.
But if you try to fit it with fewer parameters than it needs, you're just going to get some linear approximation.
So basically a Nyquist rate type of concept.
https://en.wikipedia.org/wiki/Information_bottleneck_method
what is the approximation linear in?
In the output of this nonlinear model /s.
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
it's certainly not a definite procedure for determining if an arbitrary mathematical statement is true or not. it's more like educated guess and check which definitely scales up
I started creating internal models using it, then the Gated Deltanet 2 came out( https://arxiv.org/abs/2605.22791), and it seems like an evolution of it in expressiveness. And in our tests it is really better than.
Is it just me or does this read like a re-implementation of LSTMs?
Any one knows how this holds up on long context retrieval (needle in haystack , ruler) vs same size full attention model? efficiency gains look great but that usually where linear attention hybrids fall apart.
(2025)
As it's 9 months old and they just had a major model release
For K3 read this instead: https://arxiv.org/abs/2607.24653
The main contribution of the K3 paper is Stable LatentMoE. Like some other models it compresses data sent between layers, which puts certain requirements on the router. K3 improves performance by using a more balanced expert selection strategy.
Compared to the Opus 5 "model card", which read like a standard Anthropic set of alignment principles and safety concerns, this presents a plethora of useful technical details that advances the state of the art.
Same with DeepSeek papers, they are a joy to read.
Not an expert, but looks like they did a lot more work on the RL part (9 expert models, full sandbox access for agentic tasks, etc)?
most new effort in training comes in the late phase with RL techniques
the pretraining (slurping the internet) only goes so far, the new data being used is from human preferences and agent traces (designed and/or distilled)
Rather under-discussed back then: https://news.ycombinator.com/item?id=45766937
I believe OP posted it because the new Kimi K3 has 69 KDA layers (the rest are 24 Gated MLA), I think previous large Kimi models had only MLA layers.
It's not the same KDA as used in Kimi Linear, though.
What's the difference? They are both called Kimi Delta Attention.
Another banger from Zhang et. al