Okay, last talk to bring us home, we

have Arjun and Ronak, co-founders of

Trajectory. They're doing a bunch of

interesting research around what I think

is one of the hotter topics, continual

learning.

Please welcome Arjun to the stage.

>> Cool. What's up? How's it going

everyone?

Thank you so much. I'm really excited. I

know I'm close to the last one, so

hopefully there's like remaining

attention span.

I'm here. Ronak is also back there. So

we're together.

But let's get started.

We're Trajectory and we're building the

platform for continual learning.

Who are we? I think we're working on

like a really really cool mission and

that's the most fun part. The second

most fun part is I get to do this with

my two best friends. Ronak over here

worked at One Surf and Train Speed 1

before this and and Michael who worked

on really cool robotics stuff at at Deep

Mind. And what I wanted to start off

with was the world view that we have

and how that shapes

why we think this problem is important.

And so the world view that we have is

that and it's not a hot take, we are

living in an incredible time in human

history here. Every single week it seems

like another model is coming out that is

leapfrogging the last one.

It's undeniable that models are getting

better and better.

But we think that they're getting better

on one axis and that is

IQ. These models are smarter and smarter

but always feels like when you're

talking to them, it's their first day on

the job. And so you take this analogy if

you have a Terrence Tao in your pocket,

that's great.

But Terrence Tao day one at an

accounting firm is probably not the best

accountant there.

But give him a few years or honestly

maybe even like a few days,

he'd probably be really good.

And so we have this orthogonal axis of

experience that is much more important

than we think

in conjunction with IQ. And that's what

we're calling the experience gap and

that is what we want to close as a

company.

Um

And then but the question is like how do

you how do you close that? Where does

experience come from? Well, it's already

out there right now. There's so many

hundred million is a number, but so many

tokens out there that are being

generated by these agents. You view them

or sometimes you don't even view them

and they get thrown away. But this is

all real work that they're doing

and then people are acting upon them and

then it's being thrown away. And so our

take as a company as Gabe mentioned like

our opinionated take is that uh this is

a signal that we should be learning

from.

And this is also how humans get better.

And in classic AI fashion like if the if

humans do it probably a good way to

mental model off of. Uh and so today's

agents they're still slow, expensive,

error prone over time. You implement

them, they probably don't get better.

We want to imagine what learning agents

look like where as they are used by

people, they get better and better over

time.

Uh and tangibly this has two good

benefits. Right away it gets you much

faster, better, cheaper models. Uh but

more excitingly it gets you to this goal

of systems that compound with use.

And so I want to start off by talking

about how we're approaching this problem

uh and then

uh some interesting stuff for you guys

at the end.

And so where it starts off and this kind

of dovetails off of what Harrison was

talking about before is it starts off

with traceability. We need to first

capture these interactions, capture this

experience that's being thrown away.

So that's step number one.

Uh

And then step number two and this is

where where we start getting to our

research. The way we view the company

we're building is we're building a

product to allow for continual learning,

but we're doing cutting edge research

under every single one of them

to make this possible.

And so

you have all these interactions and the

next thing we're building is this idea

of a like a model spec. This idea that

like you need to have a way to define

what do I want my agent to do and have

the agent learn against that. So we're

doing really cool research here on how

to extract user interactions, turn that

into reward, extract traces, and turn

that into exact specs of what you want

your agent to do.

And then okay, cool. You have what you

want your agent to do. What do you do

with that? Well, there's two surfaces.

One is the models, right? You want your

models to to learn from actual

interactions here. And so we're doing

really cool research on algorithms like

SDPO,

and and and and things along those lines

where we're using RL to uh

take these full long traces and improve

models over time.

But models are not the only thing. You

have harnesses as well. And we're doing

a lot of really cool research on

when you have good feedback from people,

does that go to a harness, or does that

go to a model? An example here is

if it's like a fact, like this person

this like this company has been

delisted, right? You probably don't want

to train that knowledge into the model.

It's probably context that should be

available to the harness.

And so that is how kind of how we're

splitting things, and that is the

product that we're building. Allowing

you to go from real interactions all the

way to specs of what you want your agent

to get better at, to better models, and

then better harnesses, and then being

able to deploy those those models right

away,

and then start using our product.

And so that's the world we want to get

to.

If you have built agents, which I'm sure

most of you guys have,

you probably know that there are so many

things that go wrong with every single

part of these, and then how every single

one of these is an unsolved problem. And

so the way I wanted to frame the rest of

this is

I'm going to imagine that I am like

straight from Aladdin, and I have like

four wishes.

And what are those wishes that I would

ask to like make the agent ecosystem

better. And so like we have agents

today,

we have this like gap,

and there's a level where agents get to

and after that

we can really see the exponential

effects of continual learning. And what

are those in

I'll walk through them step by step with

the same four categories I talked about.

What we would like to see on the

traceability side.

What we would like to see on the eval

side, what we'd like to see on the

harness side, what we'd like to see on

the model side. So let's start with wish

number one.

Uh and how can companies get there

faster? Uh traceability.

So two two sub wishes here. Number one

is around tracing the entire tree of

what happens, uh including sub agents.

Uh what a lot of companies do is they

trace the main action that is happening,

but they throw away tool calls or sub

agents that are made. Um and that makes

it very hard to learn from the whole

thing that you've done. So that's wish

number one. Uh sub wish number two uh is

probably the most important in here and

that

you should build your product around how

you can both capture this interaction

data, but also elicit the right amount

of feedback uh so that you can then

capture it. Um and a really interesting

thing here is I think the level one way

of thinking about this is uh okay, let's

have like a thumbs up, thumbs down and

like let's capture that. And that sounds

amazing in theory uh but it's incredibly

noisy and if you've used any coding

agent, you know you kind of just like

accept everything that the agent does.

And it's only like five commits later

that you're like, oh crap, like

this broke everything. Let me go and

undo that. And so it's the corrective

behavior, the like the edits, the undos

and the retries that both need to be

like elicited from the user, but also

captured. So that's my wish on

traceability.

The second wish is around evals. And the

mental model to use here is

your the product that your user uses

should be as similar as

as close to

what the eval is done in in for wise

should be as close to where the training

is done in.

In an ideal world, these are all the

same. There's no difference between

these. And so this means that evals are,

you know, drawn from traffic, how people

are actually using your product, both

how they're using it now and the things

that they're requesting on the frontier

that might not be possible, all super

helpful.

Second is making every task roll

outable. This is a pretty big infra

challenge, uh but it's this idea that

hey, if a user has done one thing, can I

somehow replay what they what the user

has done? And that is an incredibly

helpful boon to to moving forward in

this agentic era. And then lastly, this

goes with number one, grading through

like the real harness, uh not making a

variation of the harness, but like

grading the real harnesses that people

use in production.

So that is wish number two.

Uh wish number three, getting into the

harness,

uh

pretty important. Uh number one is and

and I think the mental model to use here

is I think a lot of products

have built their harnesses around models

that existed a year a year and a half

ago. Where the primary function of the

harness was to

prevent the agent from doing bad things.

Which was really good when agents would

randomly break or have misformatted

outputs, but now we're very much in a

let the agents cook world.

Uh and so

I think I would view building the

harness as not enforcing specific flows

that need to be done, but more what are

the primitives that your product has,

whether that's search tools or private

information, and then viewing the agent

as orchestrating those primitives and

then letting it do that.

So that's sub wish one. Uh sub wish two,

and this is how we're building our

product as well, is making the agent

interface as close to the user interface

as possible.

Uh in an ideal world, every single thing

that I can do on your UI, your agent can

do via tool call as well.

Uh I think that is a world that makes

training and continual learning so so

much easier.

Uh and then lastly, this is a small one,

uh making tool responses informative. I

think it's easy to be like, "Okay, I

just want to know the state." Like let's

say I'm like calling a search tool. I

want to know the state. I want to see if

it worked. Uh and if it's writing to a a

DB or something, it just writes to it.

And what we see often is like the

response from that tool call will be uh

done. Finished.

That sounds like good in theory, but

from an agent's perspective it's like

incredibly confusing. And if you're

trying to train or learn off of that,

there's like no signal to learn off of.

Right? You have no idea what actually

was written. You have no idea what was

actually was read. And so, that's the

last sub wish under the harness is to

make your tool responses really

informative.

So, that's wish number three. I think

like canonically I only get three

wishes, but let's imagine I have four.

Uh

and that's around the models.

Uh and we've talked about that a lot

today. Uh and that is

uh

I wish it was as easy as like just

switching to an open weight model, but

if you've tried that, you've probably

seen there's 50 other considerations

from security to safety to

uh you know, different access

provisioning stuff. So, start getting

comfortable running on open weights cuz

that is what unlocks the door to owning

owning your weights and then continually

improving on top of them.

Um and then Gabe also talked about this

this idea that like model routers are

uh going to probably play a really large

role in routing intelligence to the

exact capability of the task they're at.

And so, experimenting with with routers

is another wish I would wish cast into

the world.

And so,

that is the high level here. And so, I

think like

when we work with companies, the first

thing we often do is do an audit of of

these and like where things are. And

for the most part there's a lot of work

to be done to get here and so we work

with them to build it out and it would

be my dream if we go to a company and

they have a lot of this built out.

Granted a lot of the work we're doing is

also knowing that a lot of this we can't

expect and so we're doing a lot of cool

research on, you know, assuming that

nobody has Evals, somebody doesn't have

Evals, how can we overcome that? Or

assuming that the traceability is off,

like how can we learn from the signals

that are there? And so that is

that that is my my goals for the for the

universe here.

Uh just want to quickly go over

uh I think this is like a very very core

research and product problem and so

we're really excited with the team we've

put together but I think more

importantly and then two of the folks

who are here today

uh we're working with extremely frontier

customers. The folks who are really

really pushing the boundaries of what

agents can do in production

both today and tomorrow.

Uh and so really excited to be working

with them and another thing I want to

show is

we have

Oh, that's my email.

Uh

we have a beta and so

a really really core belief we have is

that

this capability, this ability to own

your own intelligence, should not be

something that you need to consult away

a lot of the times. It should be

expertise you build on your own because

of how important it is for your product.

Um and what that means for us is we want

to build a product that empowers any

company to

own their own models and mainly get to

continual learning from real

interactions and so here's some some

cool screenshots from our product. I'm

I don't know if Nico is here but I'm

supposed to show this Nico later today

so this is like a sneak peek. Uh

but here's Harvey. Here's the lab

benchmark that that Gabe was talking

about. Really really easy to import. Um

training a model is incredibly easy.

We're extremely proud of the interface

we've put together here cuz if you've

post trained, you know, there's like 50

million things that go wrong, 50 million

knobs to turn, and it's all researcher

intuition, trademark asterisk, whatever

that means. And what we've tried to do

is

take all of how our researchers post

train their models and

use an agent behind the scenes to take

care of most of those knobs and just

only expose the ones you need to know to

make post training as easy as possible.

So, really easy to train a model, see

how the model's doing, evaluate,

compare it,

uh see if it's better than the model you

were using before, and then deploy it.

And so, in terms of actual work you put

in, and that was not waiting for the

model to train,

probably like 15 minutes. And so, that

is the world we're we're trying to get

to to empower all these companies and

hopefully more folks in this room.

Uh

So, yeah, that's our goal. We want to

every company to own its own experience

layer, and we're excited to build that

alongside of some of y'all here,

hopefully.

>> [applause]

>> Thank you. It's a great talk. You know,

I wonder like, you know, uh when you

talk about continual learning, like uh

what do you think about their trainable

object

um between model weights, harness,

tools, application layers?

>> Yeah.

>> Uh how how do you think of them? How do

how do you prioritize?

>> Yeah, I think I like a fun fun phrase is

that if you ask like six researchers

what continual learning is, you're going

to get like

seven answers, probably. Uh

you can be very pure about this and be

like, "Oh, it needs to be like a human,

and it is just the weights that need to

be adopting in real time, one shot."

But, the way we view it is that

the intelligence that your product is

run off of is a system.

It's a system that has many components,

and true continual learning

is something that optimizes across that

system

and

optimizes based on what parts of the

system needs to be updated uh for the

information that you're learning. Like a

mental analogy that I have is like when

you are you know, saving things, right?

We no longer think of like where in your

RAM or where in your hard disk to save.

That's the level that's abstracted based

on what makes the most sense. We think

about models versus harnesses versus

context in the same way. It feels wrong

that we're having to make the decision

off of very little priors

what to update based on what.

This is a scientific problem that can be

solved. Let's do that and then let's

abstract that away.

Cool.

Oh, yes.

Yes.

>> Um, some of the speakers today talked

about, you know, the importance of not

training on your customer data.

>> Yeah.

>> And I imagine like a big part of

continual learning is you are learning

from all these interactions. Um, how are

you thinking about things like

differential privacy? Like how do you

actually make your system better with I

imagine most of these application

companies have customer specific data

arrangements that make that very hard.

>> 100%. I think that's an incredibly

important problem. Uh,

actually before this I was at Apple and

this is a problem we worked on quite a

bit as well.

Um, and there's a lot of clever things

you can do where you're not actually

training on customer data, you're

instead sampling distributions from your

customer data and then synthetically

generating your own and doing kind of

like a

mental models maybe like a cryptographic

thing where you're like comparing those

distributions to see is my data actually

on distribution, but you're not training

directly off of the customer data. Um,

and so that that's totally right.

There's a lot of interesting work there

and it's like very central and already

with our customers we're doing some

interesting and and fun things in order

to

uh, to get over that.

Yes.

>> Hey, I'm Sid. I'm from Finch. Um,

thank you.

Um,

I think episodic memory plays uh, a role

in continual learning to some extent.

For example, like if a user does

something on our platform that corrects

what an agent did before, we might want

to update an agent's behavior. Does

Trajectory have like an opinionated take

about that in the platform?

>> Yeah, I think

Okay, on episodic memory and I'm just

going to repeat here.

On episodic memory and like what if I'm

thinking about this correctly, how

different types of feedback impact what

is learned?

>> Yes.

>> Yes, okay. So many thoughts.

One way to look at this is there are

two different types of signals. There

are signals that are just

something went wrong. Like for example,

if somebody like flames the agent says

like, "You're really bad." But he

doesn't follow anything up with that or

just thumbs down or like drops off the

session. Those are cases where you know

something went wrong, but not what right

looks like. Then you have things like

the agent retried and then it got to the

correct solution or you corrected it by

saying this is a difference. And so that

is like a

difference we make. In the latter case,

we're very, very confident about the

reward associated with the with the

output and and what we're assigning.

With the former, we know to penalize

that behavior, but we don't necessarily

say like there's like a correct right

answer. That's one way of looking at it.

Another way of looking at it is there

are different

like there's like a hierarchy of like

how pertinent the information is

people-wise. There's some information

that's probably globally accurate,

globally true.

Things like a tool call repeatedly

failing when trying to call a certain

tool.

That's probably relevant to everybody.

And so this should be trained into the

model and the model should get better at

learning this tool.

A certain user is like, "I never want to

use the sub agent. Please, please,

please don't do it."

That's probably not something you should

train into the model and leave to the

context, and

what we were really excited about is

this is the stuff Harvey was talking

about where this is actually something

that will probably happen on a per org

basis, or per customer even further, a

per customer basis. And so, that's

another way to think about it as well.

So, the hierarchy of like what the

feedback is pertinent to.

>> Got it. Thank you.

>> Yeah.

Yes.

>> Thank you.

I think one of the themes of today has

kind of been this idea that there are

some, you know, tasks or part of your

product where you kind of want to maybe

like experiment more, go cheaper, add in

more open source stuff, right? And then

other areas where kind of being at the

frontier and using a closed model or a

closed framework is more useful. I guess

like extending that analogy to continual

learning, are there tasks or workflows,

or like what is the shape of problem

where something like this you've kind of

seen really matters a lot, right? Versus

where like just using, you know,

statically trained frontier model or

even a statically trained open source

model, and then like you know, using the

harness to inject relevant information

into the context is sufficient to kind

of achieve what the user usually wants

to achieve.

>> Yeah.

This is a great question. I think the

level one answer is I think for the most

part most tasks work for continual like,

but the ones I'm most excited about are

tasks

that are on the frontier. The mental

model I have that that we have for

AI progress in in in a way is

people will ask models, people will ask

products

with the expectation of what they think

the product can do.

Uh and maybe they'll ask at the edge of

what the product can't do, and then

they'll see it fail, or they'll see it

do something wrong, and they'll retreat

back and be like, oh, it's it wasn't

good enough. I can't use it for this.

I think like a great example is like,

would I even dream of typing in some of

the like the whack query that give the

cursor two years ago, like definitely

not. I was like way lower than this, but

then over time I started querying it

where the models were getting better and

I was like, "Okay, maybe we can do

bigger and bigger things."

What we're most excited about is

the way that has happened is like models

have generally gotten better and we've

been targeted pushing in certain

directions. Uh but what we're seeing

with some of our customers are cases

where

users ask for things, the model can

barely do it, but then during training

it learns how to do it and then the user

can now do this thing they couldn't do

before. That chain

is what's really exciting about

continual learning of like really

pushing that frontier

of what's possible based on what the

user tries and cannot do.

>> Awesome.

Thank you, Arjun. I think we're out of

time. Uh really appreciate you doing

this. It's awesome. Thank you.

>> [applause]