Okay, so today we're diving into this paper.
Okay.
It's all about getting AI to kind of, you know, do what we want it to, but safely.
Right.
So we're talking about those big, large language models,
LLNs, that are, you know, the writing text, they're coding, they're translating,
answering all of our questions in a really human-like way.
Yeah.
Um, but you know, how do we train these things to actually be helpful and safe?
Right.
That seems to be the tricky part.
Yeah.
And that's where this RLHF comes in.
Yeah.
RLHF, that's right.
Reinforcement, learning from human feedback.
It's like, become the main way to train these big LLNs now.
Okay.
So imagine we've got this powerful AI.
Yeah.
We want to make sure that it's actually, you know, doing what we want it to.
Right.
Aligning with our values.
Align with human values, preferences, all that stuff.
So is RLHF kind of like giving AI like a crash course?
Yeah.
You know, how to be a good AI?
Yeah.
Think of it as a loop.
Like you give it a task.
Like write a poem about a cat.
Okay.
And it generates some responses.
Uh-huh.
And then humans come in and they read them.
Like that was good.
Okay.
That was bad.
This is why.
Gotcha.
And that becomes the AI's reward signal.
Oh, so it's learning.
It's learning from these human judgments.
Okay.
Yeah.
It's like, okay, humans like this poem.
Mm-hmm.
I'm going to try and do more of that.
Exactly.
The AI is constantly adjusting its behavior based on that feedback.
Gotcha.
And the more it does what we like,
Yeah.
the more reward it gets, the better it becomes at this.
Okay, but this paper.
Yeah.
Towards reliable alignment.
Uncertainty where RLHF.
Right.
This is saying there's kind of a problem with this reward system,
right?
Yeah.
The issue is these reward models.
Okay.
That are kind of at the heart of RLHF.
Mm-hmm.
These models are trying to predict what humans would like.
Okay.
But they can be a bit unreliable.
Okay.
So imagine training 10.
Okay.
Identical reward models on the same data.
All right, so they're all learning the same stuff.
The exact same information.
You would think that they would all give the same score
to the same AI output.
Yeah, that seems pretty logical.
Right.
If they're learning from the same information,
they should come up with the same conclusion.
But what you might find is that one reward model.
Okay.
Gives an AI's output a stellar score.
Mm-hmm.
And another thinks it's absolute garbage.
Wow.
Figure one in the paper visualizes this.
Okay.
It's like imagine looking at a bar chart.
Yeah.
And instead of neat uniform bars,
they're all over the place.
Okay.
Huge differences.
So it's like asking 10 chefs to rate a dish,
and they're all giving completely different scores.
Exactly.
How do you trust any of them at that point?
Right.
And that inconsistency is a major problem for AI alignment.
Because if we can't trust the reward model,
how can we trust that it's guiding the AI in the right direction?
So this inconsistency, this unreliability,
this is what the researchers in this paper
are trying to address.
Yeah.
But why are these reward models so flaky to begin with?
It boils down to two main factors.
Okay.
Limited data.
Okay.
And randomness in training.
Okay.
So think of it like teaching a kid to bake a cake.
But you only give them a couple of recipes.
Okay.
And you let them kind of mess around with the oven temperature.
So even if the recipe is good, the lack of experience
in that random element could mess things up.
Exactly.
And the same thing happens with these reward models.
Okay.
They're trained on a relatively small amount of data.
Okay.
And there's a lot of randomness involved.
Gotcha.
In how they learn from that data.
So they're not all going to be the same.
So even if you train the same model multiple times
on the same data, you're going to get slightly different models
each time.
Okay.
I'm starting to see the problem here.
Yeah.
So how does this paper propose to fix it?
Well, they've got this really interesting approach.
And to explain it, they use this analogy.
Okay.
Called the three-armed bandit problem.
I love a good analogy.
Yeah.
Okay.
So imagine you're in a casino.
Okay.
You got three slot machines in front of you.
Okay.
Three armed bandit.
Yeah.
Each machine has a different average payout.
Yeah.
But you don't know what those averages are.
Okay.
You only have estimates.
Gotcha.
And those estimates come with varying degrees of certainty.
Okay.
So we've got these slot machines.
Yeah.
Mysterious payouts.
Yeah.
Varying degrees of certainty.
What's the best strategy?
So a naive approach would be to just go for the machine.
Yeah.
With the highest estimated payout.
Right.
Just go for the big bucks.
Sounds reasonable.
Yeah.
But remember, those estimates have uncertainties.
Right.
You might end up picking a machine that's actually terrible.
Just because the estimate was way off.
So it's kind of like gambling on a stock.
Yeah.
That everyone's saying is going to take off,
but it has no real track record.
Yeah, exactly.
Okay.
A smarter approach is to factor in those uncertainties.
Okay.
Maybe a machine has a slightly lower estimated payout.
But if you're much more certain of that estimate,
it might be the safer, smarter bet.
Okay.
So it's like diversifying your investment.
Exactly.
You're not putting all your eggs in one basket.
Right.
Even if that basket looks really tempting.
Exactly.
Okay.
I see where you're going with this.
Yeah.
So how does the slot machine analogy connect back
to the reward models in AI alignment?
Think of each arm of the bandit.
Okay.
It is a different way.
Yeah.
The AI could respond to a prompt.
So a naive reward model might say,
this response gets the highest reward.
Yeah.
But if that estimate is highly uncertain,
following it could lead the AI down a path.
Okay.
That's actually less aligned with human preferences.
So it's like the AI is blindly following that high reward
slot machine without considering the uncertainty.
And that could lead to some not so great outcomes.
Precisely.
Okay.
And that's where this paper's solution comes in.
Okay.
They're basically saying, let's not be so naive.
Okay.
Let's be more cautious about rewards with high uncertainty.
Gotcha.
They call this a conservative approach.
Okay.
And they actually bake that uncertainty into the AI training
process itself.
Oh, this is where it gets really interesting.
Yeah.
So how do they actually make the AI more conservative or risk
averse in its learning?
Instead of blindly chasing the highest estimated reward,
they encourage the AI to consider the confidence
level of those estimates.
It's like the AI is learning to say,
hold on, this reward looks really tempting.
Yeah.
But I'm not so sure about it.
Maybe I should explore other options
that have more certain rewards, even
if they're a little bit lower.
So it's learning to be a little bit more skeptical.
Exactly.
Not so prone to those risky gambles.
Right.
I like that.
Yeah.
And this cautious approach has some cool benefits.
Okay.
The researchers did some theoretical analysis
and found that.
By incorporating this uncertainty,
they could make it much less likely that the AI would
get worse during training.
That's huge, because it's like we're putting some guardrails up.
Right.
It's not going to completely veer off the rails
and become less aligned with what we actually want.
Right.
But did they actually test this out in practice?
They did.
They went beyond just the theory and actually tested this out.
First, they built an ensemble of 10 reward models
using a model called Gemma 2Bit and a massive open source
data set.
So kind of like a panel of judges.
Yeah.
Think of it like a panel of judges
with slightly different perspectives.
And did they test this ensemble out?
Oh, yeah.
OK.
They used a platform called Reward Benchmark
to see how well their conservative method performed
compared to more traditional approaches.
OK.
And guess what?
Right.
It held up incredibly well.
Nice.
So this whole uncertainty aware approach
isn't just a good idea in theory.
Yeah.
It actually works in practice.
OK.
So they've got these more reliable reward models working
together as a team.
Right.
What's the next step?
That's where PTO comes in.
OK.
Proximal policy optimization.
OK.
It's a popular algorithm for fine tuning language models.
OK.
And it's the method they chose to really test
their variance aware approach.
So they're taking an existing training method.
Yeah.
And they're adding their uncertainty awareness
to the mix.
Exactly.
They ran two experiments.
Go.
One used their cautious uncertainty aware PTO.
And the other used regular PTO.
Gotcha.
As a baseline.
So it was like a head to head competition.
Yeah.
It was like a head to head.
To see which one produced better results.
Exactly.
OK.
So I'm picturing like two AIs going at it in a training
montage.
Yeah.
This is great.
Yeah.
So how did they judge the results?
Right.
Was it just a matter of opinion?
Or was there more objective way to measure who was better?
To make sure everything was fair and square.
OK.
They brought in a completely independent reward model.
OK.
To act as the judge.
So a third party.
Yeah.
OK.
This one was based on Lama 3.
OK.
So it had nothing to do with their training process whatsoever.
It was neutral.
Yeah.
Completely neutral.
OK.
I love that.
Yeah.
OK.
Drum roll please.
Yeah.
What were the results of this AI training showdown?
Well, just like their theory of predictive.
OK.
The variance aware PTO delivered more consistent
in improvements.
OK.
It might not have hit the same peak performance levels.
OK.
Is the regular PTO.
Ah.
But it was far more reliable.
OK.
And less prone to those dramatic drops in performance.
So it's like the classic tortoise in the hair story.
Right.
Exactly.
Regular PTO is the hair sprinting.
Yeah.
Ahead.
Yeah.
But also, you know, risking a major stumble.
Yeah.
While the variance aware was the tortoise.
Right.
Slow and steady wins the race.
Exactly.
Remember that whole slot machine analogy.
Yeah.
The regular PTO was that high risk high reward machine.
Yeah.
But the variance aware was that more conservative choice.
Right.
Offering more predictable and consistent outcomes.
Yeah.
And I think in the world of AI alignment,
that's really what we want.
Absolutely.
Like slow and steady, reliable, predictable.
The special one you were talking about AI systems.
Yeah.
They could have a huge impact on our lives.
Exactly.
We want to make sure that they're not making decisions.
Right.
Based on unreliable or unpredictable reward signals.
OK, now I want to go back to those 10 reward models
that were in the ensemble.
Right.
I'm curious, like, how much did they actually
disagree with each other?
Yeah.
Because I imagine that there's got
to be some interesting insights there.
They did a deep dive into the variability
of those reward models.
OK.
So remember, they were all trained on the same data.
Right.
But they wanted to see just how different their scores were
for the same AI generated text.
So what did they find?
Was it like a polite disagreement?
Or were these models just, like, throwing shade at each other?
Well, the variance in those reward scores
could be pretty wild.
OK.
Sometimes the scores range from 3 to 14 on their scale.
So that's a pretty big difference.
Yeah.
OK.
Meaning one model might give an AI output a glowing review
while another thought it was a total flop.
Wow.
So even with that same training data,
they still had, like, their own distinct opinions.
It really highlights that these reward models
aren't these perfect, all-knowing oracles.
Right.
They're complex systems.
Yeah.
And their outputs can be influenced
by these really subtle factors that we might not even
fully understand.
It's kind of like you might love sausage.
Yeah.
But you don't want to see how it's made.
I love that analogy.
We don't always want to know the messy details
behind how things work.
But in this case, I think understanding
how that sausage is made, meaning how those reward models work
and where their uncertainties lie,
is incredibly important.
Because it's that understanding.
Yeah.
That allows us to develop these more robust and trustworthy AI
alignment techniques.
OK.
So zooming out a bit, how do you think this research is going
to shape the future of AI alignment?
Is this like a really big deal?
Yeah.
Or is it just one small step?
I think it's a pretty significant step.
OK.
By directly tackling this issue of reward model uncertainty,
this paper has opened up some really promising avenues
for research and development.
It's like they've given us a new lens through which
to view this whole process of AI alignment.
We're not just blindly chasing the highest reward anymore.
We're taking this more measured, thoughtful, cautious approach.
And that caution that awareness of uncertainty
seems like it's going to be crucial as we develop
these even more powerful AI systems.
Because we want to make sure that they're aligned with our values,
not just blindly optimizing for some reward signal.
Exactly.
It's not just about making AI smarter.
Right.
It's about making it smarter in a way that benefits humanity.
Yeah.
And I think this research is a fantastic step in that direction.
I think we've covered a lot of ground today.
Yeah.
From flaky reward models to slot machine analogies
and cautious AI.
So for our listeners who want to go even deeper,
I definitely recommend checking out the original research
paper.
Definitely.
It's full of really insightful analysis.
Yeah.
And technical details that we didn't have time to get into today.
Absolutely.
Stay curious, keep learning.
Yes.
And we'll catch you on our next deep dive.
Sounds good.