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- Hacker News
- (2025)
As it's 9 months old and they just had a major model release
by jasonjmcghee - 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.by GaggiX
- Rather under-discussed back then: https://news.ycombinator.com/item?id=45766937by cptcobalt
- 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.
by throwa356262 - HAKE
- Another banger from Zhang et. alby Topology1
- if non standard transformers like this take off are companies like etched forked?by thesiti92
- 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.by imrozim
- If you want to believe that the success of Kimi is about distillation attacks, ignore this.by delichon
- Does one have to exclude the other?by rdtsc
- It can easily be both. Also, they didn't use this innovation in K3 - K3 pre-training would have started months ago and the paper only mentions a 48B model. The people working at this level may not even be heavily involved in shipping a new iteration of K3, or at least theory contributions to it were done many months or even a year ago and after that it is all engineering.by jeremyjh
- Well said.
The distillation theory does not even make sense as Fable was only around for days (effectively) before Kimi was released.
by moralestapia - 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.by Aurornis
- 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?by Parfait__
- The distillation complaints to me sound like when a casino complains about card countingby igleria
- 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.
by WhitneyLand - I'd kindly suggest that we could also stop calling them "distillation attacks".by egeozcan
- >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.
by oakpond - ... holy cow!by trollbridge
- I believe this is the repo:by Cort3z
- 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.
by pooyamo - I'm out of the scene these days, and my field is more RL than LLMs, but my take is that all of this is telling us that meaning (and intelligence) is in the medium. If an LLM is a function mapping an input to some stateful internal representation that in-turn then gets mapped to an output, a smaller model probably doesn't have the capacity to learn that internal mapping, at least not from scratch. Or it would take a very long time, in same way that a naive sorting algorithm would still get there, but it might be computationally prohibitive in practice.
The bigger model has a better chance of gaining a foothold in that internal representation space where inputs are mapped to meanings and outputs, and eventually, it optimizes to the point where most of the weights aren't doing much. It's not immediately clear how much expressivity is required by the network to learn that space, but so far the answer seems to be in the billions of parameters.
The more interesting question to me is to what extent we should expect the models to be invariant to data. For instance, if I learn a certain type of analysis, that skill shouldn't depend on the data I'm looking at-- it should be repeatable for any given data of the same type/class. I'm curious to what extent skills are embedded in the weights versus data and "facts". My hunch for why mathematical reasoning and programming resulted in large step changes in model performance across the board is because these are inherently skills that are widely repeatable for a large class of tasks. The ability to express programmatic logic is invariant to both the language and the task at hand. And to me, that's how you get to smaller models: by focusing on the skills.
by coderenegade - There is a Sanjeev Arora paper "A Theory for Emergence of Complex Skills in Language Models" (https://arxiv.org/pdf/2307.15936) on this subject. The key idea is there is cross entropy (how "surprised" the model is with the "correct" next token, lower is better), some of which is inherent in the language and therefore unavoidable, and the rest is model error, and that this portion of the cross entropy is reduced with scaling.
And as scaling reduces a model's excess entropy, the model can become good at combinations of skills much faster than you would expect if it had to separately see and memorize every combination. They call this "slingshot generalization".
by neerajk - It is _emerging_ in the sense that complex (sic non-linear) systems can exhibit unintended or unexpected behaviors.
It is _intelligent_ in the sense that it optimizes a thing that is hard for humans to not anthropomorphize.
Things like percolation theory, swarm theory, and others are similar topics in “complex systems”. Neural networks are interesting because they combine aspects of both complex systems and dynamical/adaptive systems (ie systems with a feedback loop).
A neural network at its very core is a function fitting algorithm. It stores matrices of parameters (ie weights and biases) such that every parameter (and combinations thereof) captures a relationship of your data in exactly the same way the slope and intercept are obtained through Linear Interpolation. All of the Regularization tricks applied just are attempts to incentivize a given parameter to not encode any trends too specifically (think an instance vs a type).
In this way you can ask yourself “how many aspects of your data are required to capture it adequately?” This is what scaling offers.
by sota_pop - 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.
by pornel - > 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?
I'm not sure what you mean? You can see the intelligence of LLMs progress predictably and stably according to scaling laws. LLMs have to encode language in addition to intelligence so there's a minimum bound for them to output sensible text (you can train specialised tiny models to solve basic puzzles without language). Start at around 127M and compare models of increasing parameters and you'll see a clear progression in intelligence.
> 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
How is that a basic tenet? Simple, easier to parallelise algorithms that have lower memory requirements, or can take better advantage of hardware, or don't hit a plateau the more compute you throw at them, can absolutely beat cleverer algorithms. E.g. brute forcing rendering with Monte Carlo path tracing will give you more physically accurate results than ray tracing or rasterisation algorithms that rely on a bundle of hacks to approximate global illumination, transparency smooth shading, etc.
by joefourier - 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.
by IanCal - > 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...
by hnfong - 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
by jlamberts - 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.by bratao