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---

Title: How We Scaled Kimi K2.5 S81695 | GTC San Jose 2026

URL Source: https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s81695/

Published Time: Tue, 11 Aug 2026 16:37:09 GMT

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[Video 1](blob:https://www.nvidia.com/87536398-1903-4705-be0e-ca7d260e8732)

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00:12

Hi everyone, thank you so much for the introduction,

00:15

it's great to be here, it's great to have this opportunity

00:20

to share with you guys some of our latest progress and efforts.

00:26

So one of our major pursuits is to build better open models.

00:34

And we believe in democratizing intelligence.

00:37

With open models, you can deploy anywhere.

00:39

It can be on your local servers.

00:41

It can be on the cloud.

00:42

And you can access every single bit of the weights in the model

00:47

instead of just using a black box.

00:50

And this is one of the slides that I took from Jensen's

00:54

talk earlier this year at CES.

00:56

So as you can see, open models are quickly closing the gap

01:00

with proprietary models and it's reaching the frontier.

01:05

And we believe that with better met open models, we're going

01:07

to make intelligence more accessible to anybody in the world,

01:12

in every corner of the world.

01:15

But open models cannot be just open.

01:18

They have also to be great.

01:22

So in this talk, we're going to discuss how we

01:25

make open models great.

01:28

So as we know, scaling is a primary driver for a lot

01:34

of progress, maybe all of the major AI developments that we have

01:38

witnessed in the last few years.

01:41

And here, we're going to discuss how we scale our model in

01:44

different dimensions.

01:46

So on the left-hand side, the first figure you see here is

01:49

kind of the standard scaling law.

01:52

So on the x-axis, you have the log of the number of training

01:56

tokens, and on the y-axis, you have the log loss.

02:00

And as you scale the number of training tokens, you

02:02

get a lower loss.

02:03

But here the point is, we're not going to just scale the

02:07

number of training tokens, but we also want to improve the token

02:12

efficiency, meaning that we want to move this curve to the left-hand

02:17

side so that we can achieve a lower loss, a much lower loss, using

02:22

the same number of training tokens.

02:25

And this can be achieved by having better architectures

02:28

and optimizers, as we'll discuss in our later slides.

02:33

And the second scaling dimension that we're very interested

02:36

in is to scale the context length.

02:39

So as you can see in the second figure, if we increase

02:43

the context length,

02:45

Then we can have a much higher accuracy in terms of predicting

02:50

the token loss at a given position.

02:55

And this means that we can increase the capability of the

02:58

model to achieve more complex tasks by increasing the context stance.

03:04

So this is the second scaling dimensions that

03:06

we're going to talk about.

03:08

And the third scaling dimension is the number of agents.

03:11

So we introduced this new learning paradigm of agent swarms, where

03:15

we don't just rely on a single agent, but we also orchestrate a

03:21

swarm of agents that can accomplish the subtask in parallel, so that

03:25

we can increase the task capacity.

03:28

And we can translate all of this into the language of agents.

03:32

So if you look at token efficiency, it's mostly about having a

03:36

stronger prior so that you can have more efficiency when you do agent

03:41

RL to search for a better solution.

03:45

And when you think about long context, it's mostly about

03:48

increasing the context so that you can have a longer-running agent.

03:51

It can probably run for days or even weeks or months to accomplish

03:57

more tasks, more complex tasks.

04:01

And for agent swarms, it's another dimension that's added,

04:05

and at the end of the day, we're going to have a swarm of agents.

04:08

That each of them have a super long context, and each of them have a

04:11

very strong prior for us to search in this entire agent RL system.

04:19

All right, so we're going to start from token efficiency.

04:23

So this is one of the most classical figures in the history

04:28

of machine learning.

04:29

So it's taken from Kaplan et al.

04:32

And it basically says that if we scale proportionately

04:35

the number of training tokens, the model parameters, and

04:39

also the amount of compute, we can get lower and lower loss.

04:43

And this is one of the major breakthroughs that

04:47

the entire community.

04:48

has achieved in the last few years to get better intelligence.

04:53

But here, what we're interested in is to have better and better

04:58

token efficiency.

05:00

And here's the thing.

05:02

So one thing that I would like to emphasize is that token efficiency

05:07

is not just about efficiency.

05:09

It's actually also about improving the upper bound of intelligence.

05:14

So here's why.

05:16

So suppose you have, say, 50 trillion tokens, 50 trillion

05:22

high-quality tokens, and then you apply this new optimizer,

05:26

maybe the new optimizer, and then all of a sudden, you

05:29

have a two-time token efficiency.

05:31

So it means that

05:33

It's almost like magic that you get equivalently 100 trillion

05:37

tokens and nowadays we are scaling towards the data war

05:42

and we're hitting the data war and the amount of high quality data

05:47

is quite limited and if we suppose that is a constant amount then

05:51

we increase the token efficiency

05:53

It means that we're going to get better intelligence out of it.

05:57

It's not just about infrastructure efficiency.

05:59

It's about better intelligence.

06:03

So this is why we spend a lot of efforts in this aspect,

06:08

because it's going to push the frontier of intelligence.

06:12

And Mule Optimizer is one of the things that we have heavily

06:15

invested in since last year.

06:19

So it's a second-order optimizer.

06:22

And basically, every single gradient update is transformed

06:27

in a way that each entry is orthogonal to each other.

06:31

And this is very different from the traditional Adam optimizer.

06:35

And if you implement this optimizer properly, you can get a two-time

06:39

token efficiency improvement.

06:42

So we are the first work, we published the first work,

06:48

to demonstrate that our own optimizer is actually scalable

06:52

for LLM training.

06:53

And these are two key techniques that we employ to make it

06:58

effective for large-scale training.

07:00

So one of them is weight decay.

07:02

It is critical for scaling to larger models.

07:05

And the second is we want to ensure a consistent RMS

07:09

update compared to Atom.

07:10

So we have this adjustable coefficient that is applied to each

07:16

update, so that the resulting RMS is going to be comparable to Atom.

07:22

And to make MIOM memory efficient across all these NVIDIA GPU

07:28

clusters, we also developed a distributed MIOM optimizer

07:32

implementation that partitions the states across the data

07:36

parallel group so that we can have a very efficient implementation

07:41

for the MIOM optimizer.

07:43

And these are some of the results that were presented in the paper.

07:47

So as you can see, with the same number of parameters

07:51

and the same number of training tokens, we just replaced the

07:54

original add-on W optimizer with the new Mule optimizer.

07:58

It's going to improve the performance across

08:01

the board significantly.

08:04

But there was this new challenge that we encountered when we

08:09

tried to scale data further, when we tried to scale muon

08:13

for a 1 trillion parameter model, we encountered a new

08:16

issue about training instability.

08:19

So, as you can see on the left figure...

08:22

We observed that the MaxLogix quickly explodes

08:27

and quickly exceeds 1,000.

08:29

And the typical values for training for this MaxLogix is about,

08:36

say, 50 or maybe less than 100.

08:39

But for Mule, it quickly exceeds 1,000.

08:43

And at the same time, we observe training divergence

08:47

on the left-hand side.

08:47

If you look at the training loss, it goes down a bit, but then

08:51

at the end of the day, it explodes, and it cannot converge as expected.

08:56

So this is one of the technical challenges that we have to adjust.

09:01

And the solution to this is to introduce this new

09:04

technique called QKClip.

09:06

So basically what it says is that for each attention head in

09:10

this entire neural network, we're going to, in the forward pass,

09:13

we're going to compute the MaxLogit and then we're going to calculate a

09:18

dividing factor that can be applied to each key projection, as well

09:23

as the query projection, so that we can sort of clip the maximum value

09:30

of the query and the key to sort of Constrainted into a given range.

09:36

So that we're not going to have explosion anymore.

09:40

So these are some of the empirical results.

09:42

On the left-hand side, there are two curves, but they are strictly

09:46

overlapped with each other.

09:47

So these are the training curves before and after applying

09:51

the clipping technique.

09:52

So you can see the clipping technique does not affect

09:56

The training loss decrease at all, but on the right-hand side, if we

10:02

inspect the intermediate metric, if we inspect the MaxLogit, it's going

10:07

to be effectively constrained.

10:10

So it first explodes, as before, but at the value of 100, it's

10:16

going to be clipped at a constant value for a long time, and then

10:19

after a certain number of steps, it will just naturally go down.

10:23

So, the neural networks sort of find a way to constrain the

10:28

maximum value of the MaxLogic to ensure a stable training process.

10:34

And at the same time, it doesn't affect the training convergence

10:38

as shown in the left figure.

10:41

So we employed this technique in our K2 model training and

10:46

successfully scaled it to 1 trillion parameters.

10:50

And this is the first example of large-scale muon training

10:55

in the history of machine learning.

10:58

And the second dimension that we're very interested in is long context.

11:04

So this is another figure.

11:06

It's probably less known.

11:08

It's one of the hidden gems in these papers.

11:12

So instead of just pushing down the training laws by training

11:16

on more tokens, it has some insights from another perspective.

11:21

So as we can see, this is a comparison between

11:24

transformers and LSTMs.

11:27

So on the left-hand side, we can see that transformers

11:29

achieve a lower training loss given the same number of parameters

11:33

and the same number of training tokens as expected.

11:36

And this is why transformers become the de facto architecture

11:41

that people are using right now.

11:42

But on the right-hand side, it's really interesting to

11:45

see that transformers are actually better because it can

11:49

improve through the whole context.

11:52

So the x-axis is the token index in context.

11:54

And if you increase the token index, you can see that the

11:57

training loss of transformers actually dropped by a lot.

12:02

If you just continually increase the context length, the loss

12:06

just continuously drops down.

12:08

But if you look at the curve of LSTM, it just is saturated

12:13

after a certain number of tokens.

12:16

It means that transformers have this better capability

12:19

of capturing longer context, and this is what makes it

12:24

better, because if you go back to like 10 years ago, people used LSTM

12:30

for tasks like machine translation.

12:32

But it is not good for, for example, understanding entire code

12:35

base or running a super long agent trajectory to solve, for example,

12:42

writing Linux kernels from scratch.

12:45

It's not going to be accomplished by LSTMs.

12:47

So this is a very much needed capability in the era of agents,

12:53

because tasks are becoming harder and harder, and we

12:56

need longer and longer contexts.

12:58

So the research idea here is to develop a better architecture

13:03

so that we can efficiently scale to a longer context length and at

13:09

the same time achieve a lower per token loss at larger token indices.

13:16

And this is the motivation for which we introduced this new

13:21

architecture called Kimi linear.

13:24

And it contains this new linear attention variant called Kimi

13:31

delta attention, which improves the original gated delta rule,

13:37

GDR, by improved recurrent memory.

13:40

I will show the details later.

13:41

And at the same time, we're going to mix linear attention

13:44

layers with full attention layers using a 1-2-3 ratio

13:48

so that you can balance between this long context capabilities

13:53

and at the same time having a more efficient implementation.

13:59

So this is some of the formulation.

14:02

The basic idea is simple.

14:03

If you look at linear attention, in the original formulation,

14:08

the memory is going to be global.

14:09

So there is a global single decay factor that

14:14

is applied along the way.

14:16

So it means that

14:18

Basically, there are only two cases.

14:22

In one case, you're going to forget basically everything,

14:25

and you're not going to retain any information.

14:27

In the second case, you can choose to retain almost everything,

14:31

but at the same time, you don't have the capability to

14:34

leave out some of the unnecessary information in this long context.

14:38

So we introduced this key idea of having a fine-grained

14:43

decay factor as shown in this highlighted alpha term.

14:47

So instead of being a scalar, it's going to be a diagonal

14:52

matrix which controls the decay rate for each channel so

14:57

that we can have two possibilities.

14:59

For some of the channels, we can decay really, really

15:02

slow, meaning that we can retain this long-context information

15:06

across a very long range.

15:09

And at the same time, for the other channels, we can

15:12

quickly forget the information from the past indices to refresh

15:17

it and observe new information.

15:20

And this is to increase the expressivity of this model.

15:27

And of course, to leverage modern GPUs, we have to use

15:31

this chunkwise formulations so that we can parallelize

15:36

the computation of modern GPUs.

15:38

So the first equation here is the chunkwise

15:42

formulation of Kimi linear.

15:45

But as you can see, this is going to bring massive infrastructure

15:50

challenges because of this newly introduced alpha term.

15:54

Because now it is a matrix instead of a scalar, it cannot

15:58

easily be factored out.

15:59

So to achieve an efficient implementation, we rewrite

16:04

the entire equation into the bottom three equations.

16:09

So we introduced this matrix inversion operation, as

16:13

well as introducing

16:15

The cumulative decay factor, so that we can implement this

16:19

entire thing in parallel, without sacrificing any efficiency.

16:25

And more importantly, this is not an approximation, it's

16:29

an exact mathematically equivalent formulation, so that we can achieve

16:34

much efficient implementation without sacrificing any loss

16:38

in terms of performance.

16:41

So it's going to be as efficient as previous linear attention

16:47

variants, but at the same time, much more expressive.

16:51

So these are some of the results that we obtained

16:53

using a fair comparison.

16:56

So on the left-hand side, we see the performance on

16:59

two different types of tasks.

17:01

So MMMU is a short context task.

17:04

So for short context tasks, Kimi linear achieved

17:07

a better performance compared to MLA and GDN.

17:12

And at the same time, for longer context tasks such as RULER,

17:16

Kimi linear is also better than the other variants while being much

17:23

more efficient compared to MLA.

17:26

And when we scale the context further to, for example, 1

17:29

million tokens or even longer, it's going to be much more efficient

17:34

compared to the baselines.

17:36

And this is also the first architecture that can outperform

17:41

full attention across the board, including short context tasks, long

17:46

input tasks, and long output tasks.

17:50

So these are two key dimensions that we are interested in.

17:55

And the third dimension is the agent swarms.

17:59

So here is a diagram to showcase how we designed this agent

18:04

swarm paradigm to solve some of the more complex tasks

18:09

compared to single agent paradigms.

18:12

So here we have an orchestrator.

18:15

Or you can call it a main agent.

18:17

It's responsible for orchestrating tasks.

18:20

It has different options.

18:22

For example, we can spawn a group of sub-agents and assign

18:25

new tasks to these sub-agents.

18:28

Or you can collect the results from the return of these sub-agents.

18:33

And you can sort of perform this process in an iterative way.

18:38

And at the end of the day, you can accomplish a more complex task

18:43

compared to using one single agent.

18:45

And it's analogous to human society.

18:48

For example, if we build a company,

18:50

We need different roles and we need, for example, an orchestrator

18:55

or maybe we need a CEO to compose and assign the tasks

18:59

to different roles.

19:00

And at the end of the day, the entire organization

19:03

is going to have to move towards this same goal.

19:07

And here, for example, in this case, we have, maybe

19:10

you have the AI researchers, you have the web developers,

19:14

you have physical researchers, and they can study different topics.

19:17

And at the end of the day, you just collect the results

19:20

and spawn a group of fact-checkers and web developers and file

19:24

downloaders to assemble the results into a single report.

19:32

And this is another perspective to look at this new paradigm.

19:38

So the x-axis is the complexity of the task.

19:44

And the y-axis is the execution time.

19:46

And the complexity of the task is measured by the accuracy

19:51

of a group of models on such tasks.

19:55

So we can see with Agent Swarms, it's going to substantially

20:01

reduce the execution time compared to single agents.

20:08

It's going to be more effective, and this means

20:11

that we can scale this Agent Swarm paradigm Time to...

20:15

For example, if you run Agent Swarms with 100 or maybe even 1,000

20:20

sub-agents, you can accomplish a complex task within a certain

20:25

period of time that is tolerable for producing real economic value.

20:33

And we can certainly scale it in different dimensions.

20:36

We can scale the inputs.

20:38

For example, we can download and read hundreds of sources

20:42

or even maybe thousands of doses in parallel.

20:45

Or we can output, write a 100-page literature review in parallel.

20:52

Or we can take actions at scale.

20:54

We can perform data analysis for 10 different tasks.

20:58

And also it is orchestration at scale.

21:01

You have to learn to design subtasks and aggregate the results.

21:06

And technically, we define some new objective functions

21:10

to guide the learning process of our agent swarm system.

21:15

So there are three reward functions, reward objectives

21:20

that are considered here, compared to the conventional

21:25

single agent IRL learning.

21:27

So the first term is what we call the instantiation reward.

21:32

It incentivizes sub-agent instantiation to prevent

21:37

this serial collapse phenomenon from happening.

21:42

So basically, we don't want it to default to single-agent execution.

21:47

We want to encourage the parallel executions, especially when we...

21:54

When it's early stage in training and of course we can decay

21:59

the weight for this instantiation reward term over training course

22:04

because when it learns parallel execution we can reduce the weight.

22:10

And the second term here is Finish Reward.

22:14

And it is used because we observe one of the things in training

22:19

that some of these subtasks are just created but never finished.

22:25

So it's almost like it's going to hack the first term.

22:28

By just spawning a bunch of sub-agents, and the task might

22:31

be too complex, or maybe the task just doesn't make sense.

22:35

And here, we use this Finish Reward to basically encourage

22:39

that each of the sub-tasks should have a relatively high

22:44

ratio of completion.

22:46

Instead of just spawning a bunch of pseudo-tasks, we

22:50

need it to be meaningful.

22:52

So this is the second term that we use and of course we use

22:55

the same, you know, decay strategy.

22:58

We use the relative high weight at the beginning of training

23:01

and we decay it to a relatively low weight at the end of training.

23:05

And of course the third term is the standard term.

23:07

It's the outcome reward.

23:09

It's going to measure whether the entire task is completed and

23:15

then we're going to add these three terms in our reinforcement learning

23:21

And of course, we have to build the entire infrastructure

23:24

because right now you need to support the parallel execution

23:28

and they will need to support different reward functions

23:31

and to maximize the efficiency of the entire agent swarm IO system.

23:38

So here are three different things that we have tried scaling.

23:45

The Muon Clip Optimizer improves token efficiency.

23:49

And Kimi Delta attention in the Kimi linear architecture

23:53

improves long contacts.

23:55

And we also have the agent swarms paradigm to further

24:00

create a new dimension of scaling.

24:03

And all of this put together, we created Kimi K2.5, a new

24:08

model that we just released over one month ago.

24:12

Here's a short video to demonstrate some of its capabilities.

25:19

So yeah, there are a lot of interesting things, capabilities

25:23

that we discover from the model.

25:25

For example, it merges the visual capabilities with

25:29

coding capabilities.

25:30

So a lot of new things just emerge out of it.

25:34

It can read a video and then produce a website that sort

25:39

of replicates or style transfer The original video.

25:43

And all of this are due to successful and stable training

25:49

at the pre-training stage.

25:50

So this is also one of the most beautiful curves that

25:55

I observed in my life.

25:56

So this is the training curve of the K2.5 based model.

26:02

So as you can see, it went through over 15 trillion tokens and

26:05

of course in K2.5 we additionally trained another 15 trillion

26:09

tokens and the entire training process is just so stable,

26:14

there's no lost spike, especially when we introduced this new

26:18

Muon optimizer, we didn't observe any spike and this

26:21

smooth, stable training process produces a very stable outcome.

26:27

A very strong-based model that we can fine-tune on top of

26:30

it to achieve new capabilities as we introduced and saw in the video.

26:37

And this is also, of course, a trend on NVIDIA H800 GPUs, and you

26:42

should know in this H800 cluster contains two TB RAM and 8 GPUs.

26:50

They are connected by NVLink.

26:52

And another key innovation of KeyMeK 2.5 is that it is

26:59

the first open model with native joint vision text capabilities.

27:04

So if you look at previous open models, usually their

27:08

visual capabilities are added on top of a text base, meaning

27:12

that, for example, if you train the text models for

27:14

20 trillion tokens, and then on top of it, you do another 2 trillion.

27:19

Sort of a post-training process to add additional

27:23

visual capabilities on top of it.

27:25

But for K2.5, it's different in the sense that we fuse

27:30

the training process of vision and text from day one.

27:34

So it's called early fusion here.

27:36

We start from 0% of the progress.

27:39

So from day one, we're going to merge the vision and text

27:43

tokens and as shown in our preliminary experiments, it

27:47

outperforms our late fusion.

27:50

And some of the new capabilities that we observe also come

27:53

from this training recipe.

27:55

For example, if you want to do...

27:57

Vision to kill, you really have to merge vision and text into

28:02

a single brand to achieve that.

28:04

If you separate these two brands, it's not going to happen.

28:07

You have to align these two modalities into a shared embedding

28:11

space, a shared representation space so as to achieve this.

28:18

And another interesting thing that we observe is that these

28:22

two modalities can actually enhance each other.

28:25

So that's been long been a challenge that if you add

28:30

vision capabilities into a text model, it's going to

28:33

somewhat hurt the text performance.

28:37

But here we found that if you train it properly, these two modalities

28:41

can actually enhance each other.

28:43

So this is one of the key findings that

28:45

We observe in our training, so first, vision improves text.

28:50

So this is so interesting, so before Vision RL,

28:54

The performance in the first column, and then we have the

28:57

performance after Vision RL.

28:59

So here, Vision RL refers to a process that we only

29:04

use vision tasks.

29:05

So there is no task involved here.

29:09

We only have vision tasks.

29:10

For example, we teach the model how to count, how to answer

29:14

some of these visual QA problems without any, for example, math and

29:20

encoding problems in this space.

29:22

But we observe that it's going to improve the performance

29:25

for even, you know, reasonably heavy text tasks.

29:29

And on the other hand, text also improves vision.

29:32

If you have a very strong text space, you actually don't

29:35

need any vision SFT data in the training process.

29:39

And this is the approach that we adopt.

29:41

So it's called Zero Vision SFT.

29:44

Basically, we don't have...

29:45

We have basically zero vision SFT data, and the only SFT data

29:49

that we have is the text SFT data.

29:52

And then we do a joint RL over text and vision, and

29:56

you can see that we can achieve almost state-of-the-art performance

29:59

across the board on vision tasks without any vision data.

30:03

So it's clear that if you have a strong text space, it's also

30:07

going to improve the vision if you align these two modalities into a

30:13

shared space in your pre-training.

30:17

And also, these are some of the examples of

30:26

As I've shown in the video, it demonstrates strong capabilities

30:30

of visual design and front-end coding, and this also emerges from

30:35

our vision text through training.

30:39

So after all this, so this is all about Kimi K2.5.

30:44

And as you probably know, we released our new architecture

30:50

yesterday in our tech report.

30:52

It's called Attention Residue.

30:54

So here I'm also going to briefly talk about our new

30:58

work, which serves as a sneak peek into our next generation

31:02

architecture that we're probably going to adopt in our later models.

31:07

So here the motivation is quite simple.

31:11

Can we apply some of our techniques that we use in the temporal

31:16

dimension, and we just take some of the inspirations and then

31:21

apply it to the depth dimension?

31:26

And it starts from this residual connection.

31:28

So I still remember listening to Kimi's talk.

31:34

at a tutorial in ICML 2016, 10 years ago.

31:39

So it was a brilliant idea.

31:40

So basically, before ResNet, nobody was able to train deep networks.

31:47

If you increase the depth, if you increase the number

31:49

of layers for neural networks,

31:52

Nobody was able to train it because you observe this gradient

31:55

exposure and gradient vanishing, all these stability issues.

31:59

But then after the introduction of ResNet, we can train an

32:04

arbitrarily large number of layers.

32:06

You can stack as many layers as you want, and you don't

32:10

have to worry about the training stability issue and stuff.

32:14

And as discussed in Ilya's talk two years ago,

32:18

It basically says that residual connection is a variant of

32:23

LSTM, but just rotated 90 degrees.

32:27

So how do you understand this?

32:28

If you look at LSTM, it's a variant of recurrent net, right?

32:33

And it's a recurrent model process.

32:36

So we're going to take the hidden states from the last step,

32:41

and then we're going to have some gating mechanism, some function

32:45

to produce the current states.

32:48

And if you look at the depth dimension, the register connection

32:52

is basically the same.

32:53

We're going to take the outputs from the last layer, and then

32:56

we're going to apply some sort of function on top of

32:59

it to produce the current outputs of the current layer.

33:06

It's just the formulation is different.

33:08

For example, for a residual connection, we're going to

33:10

use a fixed addition.

33:11

We're going to add the previous hidden state

33:18

with the current output.

33:20

It's just the formulation that's different.

33:22

But the basic idea is the same.

33:23

It's a recurrent that applies in the dimension of death.

33:28

But on the other hand...

33:30

We can think about reformulating this function, instead of

33:36

having an LSTM, can we have an attention in the dimension

33:40

of that, and it's going to create new possibilities because attention

33:45

has been demonstrated to be so successful in the transformer era.

33:50

So what we're going to do is not just to take the last

33:53

hidden state, but we're going to consider all the previous

33:56

hidden states and use the attention operation, the attention mechanism,

34:00

to assemble and aggregate all of these previous hidden states

34:05

to compute the current state.

34:07

So this is exactly attention rotated by 90 degrees.

34:13

We view it as a natural generalization of residual

34:17

connections in the LSTM analogy.

34:21

Okay, and here is the detailed formulation.

34:24

So, on the left-hand side is a standard residue.

34:27

As I said, it is basically LSTM rotated by 90 degrees.

34:31

And the second figure is attention rotated by 90 degrees.

34:35

So, what we did is to collect all the previous hidden states

34:39

and have a simple attention operation on top of it to

34:43

produce the current layers outcome.

34:45

And of course, to increase the efficiency, to reduce

34:49

the infrastructure, for example, communication and memory overhead,

34:53

we also designed a new variant called block attention residue

34:57

on the right-hand side.

34:59

So basically, the idea is also simple.

35:02

We're going to divide all the layers in the neural networks

35:06

into multiple blocks.

35:08

For example, each block can contain, say, 16 layers, or

35:11

it can contain maybe four layers.

35:13

And then for each block, we're going to apply this

35:18

attention residue only on the output of each block.

35:21

But within each block, we also still adopt this standard residue.

35:25

So this is going to reduce a lot of overhead while having minimal

35:30

loss in terms of training accuracy.

35:33

And these are some of the impressive results that we

35:35

achieved on this new architecture.

35:39

So, on the scaling law, we can improve the token efficiency

35:44

by 24%, meaning that if you have 50 trillion high-quality

35:50

tokens, now you just magically have over 60 trillion tokens.

35:58

For the validation loss, you can also observe that it's

36:02

consistently lower than the original curve, demonstrating this

36:08

stability across optimisation, and also achieves the best improvement

36:14

on some of these coding, math, and reasoning heavy tasks,

36:18

as shown in the benchmark results of GPQA, Math, and HumanEval.

36:25

So the entire community keeps moving forward and we're happy that

36:30

we can, we're able to contribute to the community with new technologies

36:35

and some of these technologies have been sort of standard

36:41

and de facto for a long time, but as you can see, we still see a

36:45

lot of opportunities to improve it, to have revolutionary new design

36:52

to achieve better performance.

36:53

If you multiply all these scans together, you can actually

36:57

have a much better model.

36:59

So Adam was invented in 2014, and now we scale an

37:04

open-source MuonClip, a drop-in replacement for Adam.

37:09

And I'm sure that if you're training Transformer LLM,

37:14

it's going to be much better if you use MuonClip instead of Adam.

37:18

And Attention was invented over eight years ago.

37:22

And then now we have Kimi linear, which is a linear version.

37:25

We don't have to use full attention across all layers.

37:29

We can have linear attention that performs better on short,

37:32

long contacts at the same time.

37:35

And also, residual connections are now

37:39

Also a challenge, we scaled an open-source attention residue.

37:44

So I think one of the interesting things about our error is

37:49

that we sort of adopt a different mindset for doing research.

37:54

So if we go back to 10 years ago, it's mostly about publishing a

37:58

new idea, but then I think the lack of the rigor of the experiments

38:04

It's very hard to produce solid experimental results.

38:09

But now we have this scaling ladder.

38:10

We have enough resources to train the model and run it

38:16

at different scales.

38:17

We can have a whole set of benchmarks to measure the progress.

38:21

So it becomes easier to make confident and solid

38:26

conclusion out of it.

38:27

And this is one of the reasons why we are observing New

38:31

progress on this.

38:34

Ancient techniques, and I'm sure that we'll see more and

38:36

more, especially in the open source community.

38:40

I think we're going to have more and more even better

38:43

architectural and optimization improvement in the next few years.

38:50

All right, so to summarize, we're going to keep scaling our models.

38:54

And so these are three dimensions.

38:58

For example, we see different architectures and optimizers

39:04

that optimize all three dimensions.

39:07

And we'll keep. We see new dimensions for scaling, agent

39:12

swarms is not the end, and we are glad that we can move

39:16

forward with the entire open source community to achieve

39:20

better and better intelligence.

39:23

Thank you so much.

# How We Scaled Kimi K2.5

Zhilin Yang,Founder and CEO,Kimi (Moonshot AI)

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Explore how we scaled Kimi K2.5 by pioneering the Muon optimizer to double token learning efficiency and maximize training throughput through Day 0 infrastructure co-design. Gain insights into AI-native training and linear attention architectures to help you unlock the potential of longer-running agents.

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Events & Trainings:GTC San Jose

Date:March 2026

Industry:All Industries

Topic:Developer Tools & Techniques - Large Language Models (LLMs)

Level:General Interest

Language:English

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