Okay, so let's dive into this world of open source AI.
Sounds good.
We're taking a deep dive into the two U3.
Pushing Frontiers in Open Language Model
post-training research paper.
Okay.
I think you've probably heard whispers
about how this model has kind of shaken things up.
Right.
And you're in the right place to find out why.
Yeah, this is definitely a really interesting paper.
And what's really capturing everyone's attention
is how this model, which was built by just like a small team,
is challenging some of the biggest names in AI.
Oh, wow.
Even those like super secret closed source ones.
It's like a David and Goliath situation in the AI world.
Exactly.
So for those of us who aren't, you know, AI experts,
what exactly is two U3?
So imagine a family of language models
that anyone can access and tinker with.
That's what two U3 is.
Open source AI.
They started with a solid foundation called Lama 3.1.
Okay.
They added their own secret sauce
to create something truly extraordinary.
Okay, so they're not building from scratch.
They're taking something that already exists
and improving it.
Exactly.
Okay.
Think of it like taking a basic cake recipe.
Okay.
And adding unique ingredients and techniques
to transform it into a gourmet masterpiece.
I like that analogy.
Yeah.
So in this case, the team used a four stage process
to train two U3.
Right.
It starts with the called supervised fine tuning.
Right.
And they teach the model to follow instructions
using really carefully selected data.
Yeah, it's like showing the model good examples
of how to respond.
Okay.
Like a teacher giving a student model answers.
Yeah, like a study guide almost.
Exactly.
But here's where it gets really interesting.
They went above and beyond to make sure
that the data they used was top notch.
Okay.
They even developed tools
to decontaminate existing data sets,
removing any overlap with benchmark tests
to avoid it inflated scores.
So it's like making sure the students
aren't sneaking peaks at the answer key before the test.
Exactly.
Yeah.
That level of rigor is impressive.
It is.
Yeah.
They're committed to building a model
that can truly generalize and tackle new challenges.
Okay.
After the supervised fine tuning,
they move on to what's called preference fine tuning.
Right.
Which is all about refining the model's responses
based on what humans prefer.
So they're taking into account what we,
like what we humans find helpful, interesting, or enjoyable.
You got it.
And for this stage,
they used a fascinating mix of human
and AI-generated feedback.
Oh, wow.
It's like having a panel of judges
with different perspectives,
helping the model to improve.
That's really clever.
Yeah.
It's like they're combining the best of both worlds.
Human intuition and AI's ability to process
massive amounts of information.
Right.
What's the third stage of training?
The third stage is where they really break new ground.
Okay.
It's called reinforcement learning
with verifiable rewards, or RLVR for short.
RLVR.
Yeah.
Okay.
And this technique is designed for tasks
with clear right or wrong answers,
like math problems or coding challenges.
So it's like giving the model points
for every correct solution.
Solutioning it to become a master problem solver.
Right.
Okay.
And the final stage is all about transparency.
Okay.
They've made all of their data, code,
and even their training recipes public.
Wow.
This is a huge deal in the AI world
where things are often kept secret.
Yeah.
Very secretive.
Yeah.
So they're basically inviting everyone to explore,
learn from, and build upon their work.
Exactly.
That's the essence of open source, right?
That's it.
Okay.
Let's back up for a second.
Sure.
You mentioned how they decontaminated data.
Right.
What exactly does that mean,
and why is so important?
So it turns out that a lot of data sets
used to train AI models contain information
that overlaps with the benchmark tests used to evaluate them.
Oh, okay.
It's like giving a student the test questions in advance.
Oh, I see, yeah.
This contamination can lead to artificially inflated scores.
Yeah.
Making a model seem smarter than it actually is.
Right.
Ah, so they're ensuring that the model
isn't just memorizing answers,
but actually learning how to solve problems.
Like that's a crucial distinction.
Yeah, it is, yeah.
Did they talk about how they chose the data for training?
They did.
Okay.
It seems like that's a key factor
in building a well-rounded AI.
It is.
They emphasized using data from a wide range of sources.
Okay.
They tapped into real user interactions with models.
Oh, wow.
Imagine learning from how people actually talk to AI.
That's cool.
They also included data sets for specific skills
like math, coding, and even multiple languages.
Nice.
It's like giving the model a diverse education
to make it as versatile as possible.
That makes a lot of sense.
Yeah.
So, is there anything about how they generated
some of their own data?
They did, yeah.
Okay.
That sounds intriguing.
It is, yeah.
They actually used AI to generate some of the training data.
Oh, wow.
But they were careful to avoid creating repetitive patterns
that can make AI-generated data less effective.
Right.
So they used a clever approach
where they created different personas
for the AI to role play.
Oh, interesting.
Like a machine learning researcher or a beginner coder.
This helped to diversify the generated data
and make it more realistic.
So they've carefully curated their data.
Right.
Now let's talk about the specifics
of how they train the model.
Yeah.
It seems like this multi-stage process
is where their innovation really shines through.
I think so too.
In the first stage, supervised fine tuning,
they collect a massive amount of data
containing prompts or instructions
paired with the desired responses.
Okay.
It's like giving the model examples
of good conversations to learn from.
I see.
And this is where all those diverse data sources
come in handy, right?
Yeah.
Real user interactions, skill-specific data sets,
and even the persona-generated data.
All coming together.
Do not just throwing everything in at random, are they?
They're not, no, they're very strategic about it.
Okay.
They refine the data mix
through multiple rounds of training,
tweaking the recipe as they go to get the best results.
So it's a very iterative process.
It is.
Constantly adjusting and improving.
Yeah.
What kind of results did they see
after this first stage of training?
Well, the paper includes a table
that compares the performance of their model
called 2U38B-SFT.
Okay.
Against other open source and even some closed models.
Okay.
And let me tell you, the results are impressive.
I'm guessing 2U3 came out on top?
You bet.
Nice.
It's significantly outperformed prior state-of-the-art models
across a range of benchmarks,
including MMEO, Truthful QA, and GSMA-K,
which tests math problem solving skills.
Wow, those are some tough benchmarks to beat.
They are.
That really speaks to the effectiveness
of their data curation and multi-stage training approach.
Yeah, they're really putting a lot of thought into this.
Yeah.
But I'm curious about this length normalized,
direct preference optimization.
Okay.
What is that exactly?
Sounds pretty technical, right?
Yeah, a little bit.
Well, direct preference optimization, or DPO,
is the key algorithm they use
for the second stage of training.
Right.
Preference fine tuning.
Remember, this stage is all about making the AI's responses
more aligned with human preferences.
Right, so they're essentially teaching the model
to be more human-like in its communication.
Yeah.
Okay.
How does this DPO method work?
It's a way of training the AI directly on human preferences
without needing to create a separate reward model,
which can be very complex and time-consuming.
I see.
The length normalized part is a clever tweak they made
to the algorithm to make it even more effective.
So they're simplifying the training process
while making it more effective.
Exactly.
That's impressive.
Yeah, they're pretty clever.
What kind of data do they use
for this preference fine tuning stage?
They use a mix of sources.
Okay.
But they really focus on capturing human judgments
about the quality of the AI's responses.
Okay.
They even developed a system
where they use different AI models as judges
to rate the responses generated by their model.
Wait, they're having AI judge other AI.
Yeah, it's a little bit meadow, isn't it?
It is, yeah.
But they're essentially leveraging the capabilities
of different AI models to provide diverse perspectives
on the quality of their model's responses.
It's like having a panel of expert judges
with different areas of expertise.
That's an interesting way to get a more well-rounded
assessment of the AI's performance.
It is.
Do they mention what kind of improvements they saw
after using this DPO method?
They found that DPO led to significant improvements
in several areas, including Alpacaevigl,
which tests the model's ability to follow instructions,
and Safety, which evaluates its ability
to avoid generating harmful or offensive content.
So after two rounds of intense training,
they've got a model that's not only smart,
but also safe and well-behaved.
That's right.
Now let's move on to their final.
Okay.
And perhaps most innovative stage of training.
Reinforcement learning with verifiable rewards,
or RLVR.
Right.
This one sounds like it's pushing the boundaries
of what's possible with AI training.
It is.
It's where it gets really interesting.
RLVR is all about focusing on tasks
where we can clearly determine
if the AI got the answer right.
Okay.
Like math problems or coding challenges.
Right, like a very specific grading rubric.
Exactly.
No room for subjectivity here.
Right.
How does it actually work?
Well, in RLVR, the AI receives a reward.
Think of it like a point.
Okay.
Only when it produces a correct solution.
Right.
This reward system helps to sharpen
the AI's focus on accuracy.
Okay.
Making it a master problem solver.
And they're training it
on some challenging problems, right?
They are.
They specifically focus on three domains.
Math problems from the GSM 8K dataset.
Okay.
More complex math problems from the math dataset.
Wow.
And precise instruction following
from the IEVL dataset.
So they're covering a broad spectrum of tasks
that require accurate solutions and logical reasoning.
Yeah.
Did they encounter any hurdles
while implementing this RLVR technique?
They did.
Yeah.
Training a large language model
with reinforcement learning
can be very computationally expensive.
Yeah, that makes sense.
It takes a lot of processing power
to teach a massive AI system
by giving it points for every correct answer.
Right.
So what do they do to overcome this challenge?
Well, they built a specialized system
to run the RLVR training process.
Okay.
This system leverages a technique
called asynchronous RL training.
Okay.
Which improves efficiency
by allowing different parts of the training process
to run concurrently.
So it's like having multiple coaches
working with an athlete at the same time.
Exactly.
A much faster way to train.
What were the outcomes
of this final stage of training?
The results are impressive.
Okay.
They found that RLVR further enhanced
the model's performance on those targeted tasks,
particularly on GSMAK, math, and IEVL.
Is that working?
It's working.
Okay.
This shows that their approach is effective
for both general language tasks
and those requiring precise reasoning.
That's incredible.
It is.
So they've taken this open source AI,
put it through this really rigorous training program,
and it's performing at a level that rivals
or even surpasses some of the big names in AI.
You're absolutely right.
And it all comes back
to their commitment to transparency.
They've openly shared their data,
code, and training methods,
which is a game changer in the world of AI.
It is.
They're essentially inviting the entire AI community
to learn from their work and build upon it.
It's like they're saying,
let's all work together to build a better AI future.
Exactly.
But hold on a second.
We've talked a lot about training and data.
Right.
But what about evaluating 2U3's performance?
Oh, that's a great point.
How do we know it actually works?
You raise a great point.
Evaluation is a critical part of any AI research.
And the 2U3 team didn't just rely on standard benchmarks.
Okay.
They went a step further
and created a new evaluation framework
called the Open Language Model Evaluation System,
or Ulmes.
Ulmes.
Yeah.
Okay, so it's like designing a standardized testing system
for AI to ensure that everyone's playing by the same rules.
Exactly.
What's special about Ulmes?
Ulmes includes a collection of carefully selected benchmarks.
Okay.
Covering a diverse range of tasks and skills.
Right.
But here's the key.
They divided the evaluation suite into two sets.
Oh, okay.
One set, called the Development Suite,
is used throughout the training process
to monitor the model's progress
and make adjustments as needed.
So it's like giving the AI regular quizzes
and practice tests throughout its training.
Exactly.
But what about the other set of evaluations?
The second set, called the Unseen Suite,
is kept hidden until the very end.
Okay.
It's like a final exam.
Oh.
Where the AI has never seen the questions before.
Okay.
This is the ultimate test of the model's ability
to generalize and solve problems
it hasn't specifically been trained on.
That's a clever way to assess its true capabilities.
It is.
What kind of challenges do they include
in this Unseen Suite?
They included a range of new and challenging tasks,
like AGEvol, which focuses on complex reasoning,
and HREF, which evaluates the model's ability
to follow instructions across different categories.
Wow.
They really wanted to push the model to its limits
and see what it could do.
So they didn't just create this powerful AI.
Right.
They also developed a really rigorous way
to assess its capabilities.
That's right.
That's a commendable approach to research.
It is.
And in the spirit of transparency,
they even shared some insights into what didn't work
during the development process.
Oh, interesting.
For example, they experimented with different methods
for generating data for preference fine tuning,
but found that some approaches weren't as effective
as others.
So they're not just showcasing their successes.
They're also highlighting their challenges
and the lessons they learned along the way.
Exactly.
That's incredibly valuable for other researchers
and developers building on their work.
It is.
And they also discussed areas for future research,
like improving the model's ability to handle
longer conversations, expanding its multilingual capability,
and exploring ways to integrate it with tools and agents.
It sounds like they've only just begun
to explore the possibilities of open source AI with 2U3.
It does, yeah.
This research is truly groundbreaking,
not just for the technical advancements,
but also for their commitment to transparency and collaboration.
Yeah.
It's an exciting glimpse into the future of AI.
It is.
Welcome back.
I'm ready to hear how well 2U3 did on those hidden tests.
Right.
It's like waiting for the results of a big exam.
Well, the results are in.
And it's safe.
I have to say that 2U3 passed with flying colors.
OK, that's a relief.
Yeah.
But I'm guessing it wasn't a perfect score.
Right.
Were there any areas where 2U3 struggled?
So there were a few interesting observations.
OK.
You see, while 2U3 excelled at math problems
in the development suite, its performance
dipped a bit on a similar but unseen set of math problems
called deep-mind mathematics.
So even though it aced the practice tests,
it stumbled a bit when faced with brand new challenges.
Exactly.
Why do you think that happened?
Well, the researchers noticed that 2U3 had a tendency
to over-apply formatting rules.
OK.
It had learned during training.
For instance, in the math data set,
answers are often expected in latex format,
which is a specific way of writing mathematical formulas.
I see.
2U3 seemed to apply this formatting even when
it wasn't necessary, and that sometimes tripped it up.
It's like a student who's so focused on using perfect grammar
that they forget to answer the question itself.
Exactly.
That's funny.
It highlights how even subtle aspects of the training data
can influence the model's behavior.
Right.
Yeah, because it's picking up on these patterns,
but not necessarily understanding
like the underlying concept.
Exactly.
Were there any other unexpected findings
in these unseen evaluations?
Yes.
They found a significant difference in performance
between iFEVL, a benchmark they used during development,
and a similar but unseen benchmark they created called
iFEVLode.
OK.
I remember we talked about out of distribution tasks.
Right.
So iFEVLode is designed to test how well the model handles
instructions it's never seen before.
Exactly.
Both benchmarks focus on precise instruction following,
meaning the AI has to understand and execute
very detailed instructions.
Right.
So it's like giving the AI a very specific recipe
and seeing if it can follow it perfectly.
Exactly.
So how did two year three do?
Well, here's the surprising part.
OK.
Even models that did exceptionally well on iFEVL,
including two year three, saw a noticeable drop
in performance on iFEVLode.
So it's like they ace the practice test
but struggle with the real exam.
Right.
What does that tell us?
Well, it suggests that even though the model appears
to be mastering precise instruction following,
it might be overfitting to the specific examples
it encountered during training.
OK.
In other words, it's not truly grasping
the underlying principles, which
makes it harder to generalize to new instructions.
It seems like the AI is still learning by rote to some extent
rather than developing a deeper understanding of how
to follow instructions.
That seems to be the case.
OK.
And it highlights an ongoing challenge in AI,
developing models that can truly generalize and learn
like humans do, adapting to new situations
and applying knowledge flexibly.
A good reality check that we're still
in the early stages of AI.
Yeah.
And there's a lot more to learn.
Right.
But even with those limitations, the results of 2U3's
evaluations are still pretty impressive.
They are.
They show that open source AI can be just as good as or even
better than proprietary models in many areas.
Absolutely.
And the team's commitment to transparency
is incredibly valuable for the entire AI community.
It is.
By sharing their methods and data,
they're encouraging collaboration and driving innovation.
Exactly.
It's like they're saying, let's all
work together to build a better AI future.
So where do we go from here?
That's a great question.
What are the next steps for 2U3?
And what are the broader implications
of this research for the future of open source AI?
Well, they've identified several exciting avenues
for future research.
OK.
One area they're keen on exploring
is enhancing the model's ability to handle longer
conversations and more complex interactions.
Right now, 2U3 is primarily trained on shorter conversations.
Right.
But imagine an AI that can understand and engage
in more nuanced dialogues.
Right.
Like a real back and forth conversation.
Exactly.
That would be huge advancement for things like chatbots,
virtual assistants, and AI-powered writing tools.
All sorts of things.
They're also looking into expanding
its multilingual capabilities.
Yes.
So beyond English.
Beyond English.
Imagine an AI that can seamlessly communicate
in multiple languages.
Wow.
It would open up so many possibilities
for cross-cultural communication and collaboration.
That's an inspiring vision.
It is.
Making AI accessible to people from all over the world.
Precisely.
That's amazing.
It aligns with the idea of democratizing AI.
Right.
And ensuring that everyone can benefit from its potential.
Yeah.
Another area they're exploring is integrating 2U3
with tools and agents.
So not just communicating with words,
but also interacting with the world around it.
That's the idea.
Wow.
Imagine an AI that can access external tools and systems.
Correct.
Search engines, databases, even physical devices.
Wow.
So it could actually take actions based on what
it learns and understands.
Right.
The possibilities seem endless.
They really are.
It shows how open source AI can be a driving force
in developing really versatile AI systems that
can address a wide range of real world problems.
I agree.
This research is truly pushing the boundaries of what
we thought was possible.
It is.
It's exciting to think about how 2U3 will continue to evolve
and inspire new innovations in the AI community.
It is.
It is a very exciting time.
I'm glad we're here to talk about it.
Me too.
Welcome back for the final part of our deep dive into 2U3.
It's been quite a journey.
We've covered a lot of ground.
We have.
Exploring this technology, the training process,
and even those unseen evaluations.
Yeah.
It's really fascinating to see how this open source model is
kind of shaking up the AI world.
Yeah.
Challenging those preconceived notions.
Right.
And pushing those boundaries of what's possible.
Exactly.
But now it's time to step back and consider that bigger picture.
Right.
What does the rise of powerful open source AI mean for us?
Like what are the implications for society,
for the future of work, and even for our understanding
of intelligence itself?
Yeah.
Those are the million dollar questions, aren't they?
They are.
And they're not easy to answer.
No.
But it is crucial that we start having these conversations
now as this technology continues
to evolve at such an incredible pace.
Absolutely.
So let's dive into some of these big picture questions.
OK.
First up, what does the rise of open source AI
mean for the future of work?
Well, one of the most immediate impacts we're likely to see
is an acceleration of automation.
OK.
Tasks that were once thought to require human intelligence,
like writing, coding, and data analysis,
might become increasingly automated as AI, like tool
U3, becomes more widely available.
That makes sense.
Yeah.
If AI can do these things faster and more efficiently.
It's only natural that businesses would adopt these tools.
Right.
But I can also see how that could lead to concerns
about job displacement.
You're right.
It's a valid concern.
Yeah.
That's why it's essential that we
start thinking about how we can adapt our workforce,
invest in retraining and education,
and create new opportunities that leverage
the unique strengths of both humans and AI.
It's about finding that synergy, that sweet spot,
where humans and AI can work together
to achieve incredible things.
Exactly.
And open source AI could play a key role
in making these advanced tools more accessible
to a wider range of businesses and individuals.
Right?
Absolutely.
It's not just large corporations that
can benefit from this technology.
Right.
Smaller businesses, startups, even individual entrepreneurs
could have access to these powerful tools.
That's great.
Which could lead to a surge in innovation and creativity
across all sectors.
That's a really exciting prospect.
It's like leveling the playing field.
It is.
And empowering everyone to participate in this AI revolution.
Exactly.
What about the implications for education?
Oh, yeah.
I can imagine AI having a profound impact
on how we learn and teach.
Imagine a world where students have access
to personalized AI tutors.
OK, yeah.
That can adapt to their individual learning styles,
provide instant feedback, and make
learning more engaging and interactive.
It's like having a personal coach for every subject,
guiding you along the way.
Exactly.
That could be incredible.
Yeah.
But I can also see potential downsides.
What about concerns that AI could stifle creativity
or discourage critical thinking?
Those are valid concerns.
And it's essential that we address them thoughtfully.
We need to ensure that AI tools are used responsibly
in education, that they complement rather than replace
human teachers, and that they encourage students
to develop their critical thinking skills
and explore their own unique creative potential.
It's about striking that balance,
using AI to enhance the learning experience,
while preserving those really essential human qualities.
Absolutely.
Now, I can't let you off the hook without tackling
one of the biggest questions of all.
Oh, OK.
What does the rise of AI, like 2U3,
mean for our understanding of intelligence itself?
The age-old question.
As AI systems become increasingly sophisticated,
they're challenging our traditional notions of what
it means to be intelligent.
They can perform tasks that were once thought
to be uniquely human.
And they're constantly learning and evolving.
It's like we're witnessing a new form of intelligence
emerging, and it's forcing us to rethink our own place
in the world.
Exactly.
And the beauty of open source AI is
that it allows us to explore these questions together
as a global community.
That's great.
By sharing our knowledge and insights.
We can collectively navigate the complexities of AI
and ensure that its development benefits all of humanity.
That's a powerful message.
It is.
It's not just about the technology itself.
Right.
It's about the collaborative spirit, the open exchange
of ideas, and the shared responsibility
we have in shaping the future of AI.
Well said.
Well, it's been a privilege to explore the world of 2U3
with you today.
Likewise.
This deep dive has been a fascinating journey
from the technical details to the philosophical implications.
I agree.
And I think it's safe to say that open source AI,
like 2U3, will continue to shape our world
in really profound ways.
It will.
It's an exciting time to be witnessing these developments
and participating in this conversation.
It is.
Thanks for joining us.
Thanks for having me.