Hey everyone, welcome back, ready for another deep dive.
Today we're looking at a paper that's trying to,
well, it's basically trying to figure out
how to make AI better at thinking.
Oh yeah.
Like really thinking things through.
Yeah, that's a big one.
Especially for, you know, like those really complex problems.
Absolutely.
The kind of stuff that you need for advanced math
or those tough STEM problems.
So this paper is called,
Enhancing LLM Reasoning with Reward Guided Tree Search.
And it's kind of trying to crack the code
of how to get large language models to be better reasoners.
Right, because they're good at so many things,
but this is still a big challenge.
And especially, you know,
open AI has been kind of secretive
about how they made their model a one so smart.
They haven't exactly given us the recipe.
Exactly.
So this paper's like,
all right, we're gonna try to figure this out.
So whether you're an AI expert
or just curious about this stuff,
we're gonna break this down in a way that makes sense.
Yeah, we'll make it clear and fun, I promise.
So you're right, LLMs have come a long way,
but when you start talking about multi-step reasoning,
you know, like if you think about solving a geometry proof
or figuring out a complex physics problem,
it's, that's a different ball game
than, you know, writing a poem or something.
Absolutely, it's a different kind of thinking, right?
Like it's not just about being creative
or fluent with language.
It's about logic and, you know,
being able to think step by step.
Right, exactly.
So this paper is basically saying,
what if instead of just making these models bigger,
we make some think more during a task?
Yeah, like give them a little more time to process.
Yeah, like how we humans might, you know,
pause and jot down some notes
when we're faced with a tough problem.
That's where this whole idea of test time scaling comes in.
Exactly, and the paper presents a framework
for doing just that.
And it's got three main parts that work together,
almost like a team tackling a puzzle, right?
Oh, I like that.
So first, you've got the policy model.
Think of that as our puzzle solver,
trying out different pieces.
Okay, got it.
The one trying to find the solution.
Right, then there's the reward model.
This one acts like the coach,
giving feedback on which moves look promising.
So it's like, hey, good job, that piece fits there,
or uh-oh, that's not quite right.
Exactly, and finally, you've got the search algorithm.
This is our strategy guide,
making sure the policy model explores the puzzle efficiently.
So it doesn't waste time on dead ends, right?
Exactly.
Makes sense.
Okay, so let's zoom in on that policy model for a second.
How do they actually train it to be a better reasoner?
What I found really fascinating was this idea
of using a bigger, more powerful LLM
to create example solutions.
Yeah, it's a clever trick.
And it's all about showing the policy model
the right format for reasoning.
Ah, so it's not just learning from any old data,
it's learning from the best.
Precisely, it's like learning from a master chef.
Instead of just watching them cook,
you get to see their detailed recipe notes,
every step laid out, all the little tips and tricks.
Okay, I get it.
So the policy model is basically learning by example
by seeing how the pros do it.
Exactly, and to make it even more interesting,
they use something called preference optimization.
Ooh, that sounds fancy.
It is, so the policy model actually
generates multiple potential solutions,
and then the reward model steps in and judges them,
like which one seemed the most likely to be correct?
Ah, so the reward model's like,
okay, solution A is better than solution B, because...
Exactly, it's a constant back and forth.
So it's like the policy model's always getting feedback
and refining its approach.
Exactly, it's always learning and improving
based on what the reward model prefers.
Okay, that's really cool.
So we've got our problem solver getting trained by a coach,
but what about that search algorithm?
How does that sit in?
Well, that's where things get even more strategic,
because it's not just about trying every possible solution,
it's about exploring the solution space in a smart way.
Right, like a good chess player thinks several moves ahead.
Exactly, and that's where these search algorithms
come into play.
Interesting, so they're not just randomly trying things,
they're being strategic about it.
Right, so they tested out a couple of different
search algorithms to see which worked best
for these reasoning tasks.
The first one they tried is called MCTS,
which stands for Monte Carlo Tree Search.
It's a popular algorithm for problems
with lots of possibilities, like in games like chess or Go.
So it's like a strategic planner looking ahead
to see the consequences of different moves.
Exactly, and then they tested out a modified version
of MCTS called MCTSG.
This one is a bit more adventurous
considering all the potential next steps
before picking the most promising one.
Okay, so it's like, let's look at all the options
and then pick the best one.
Precisely, and what's really interesting is that
for these math problems, the simpler MCTSG
actually outperformed the standard MCTS.
Oh wow, so sometimes a more straightforward approach
can be surprisingly effective.
That's right, it suggests that the way humans
intuitively break down complex tasks
might be more aligned with this simpler search strategy.
Hmm, that's fascinating.
So I've got this whole framework,
but does it actually work?
Well, to find out, they tested it
on four different math data sets,
and we're talking challenging stuff here.
Problems from math Olympiads, college level courses,
the kind that would make most people sweat a little.
Okay, so no easy questions here.
Nope, and the results were pretty promising.
Not only did their framework beat a basic LLM
on all four data sets, but it also beat approaches
that rely on generating tons of random guesses.
So it's not just about brute force,
it's about being smart and strategic.
Exactly, and that's one of the key takeaways
from this research.
Okay, that's really cool, but I'm curious about
how they actually trained the policy model
to think step by step.
Yeah, how did they teach it to approach these problems
in a logical, structured way?
That's where things get really interesting.
Well, remember we talked about using a bigger LLM
to generate those example solutions?
Yeah, like the Masterchef's recipe notes?
Right, those examples weren't just about
giving the final answer, they were about
showing the entire thought process, you know?
Oh, so like not just, here's the cake,
but here's how you bake the cake step by step.
Exactly, it's like having a math tutor
who shows you their work every step labeled and explained.
I see, so the policy model is learning by example
by seeing how the pros break down the problem.
Precisely, and to make sure it really gets that structure,
they trained it on this data set called NUMINA Math.
It's full of math problems solved
in that detailed step by step format.
Okay, so lots of good examples to learn from.
And they also did this cool thing called Instruction Tuning.
Instruction Tuning, what's that?
So basically they gave the policy model
clear instructions on how to approach the problems,
like analyze the question, rephrase it in your own words,
and then break down the solution into labeled steps.
Ah, so it's like giving it a checklist
for good problem solving.
Exactly, it's not just learning by example,
but also getting explicit guidance
on how to think things through.
Got it, so they're really trying to make sure
it learns the right approach,
but this all still depends on that reward model
being able to judge the solutions, right?
Oh, absolutely, the reward model is key.
So what were some of the things they tried
with the reward model's training?
Well, they experimented with a bunch of different approaches
to see what worked best.
Like they tried simple scoring,
you know, so thumbs up or thumbs down.
But they also tried more detailed evaluations
where the reward model had to actually explain its reasoning.
Oh, interesting, so kind of like a teacher
who might just circle a wrong answer,
versus one who writes comments explaining why it's wrong
and how to improve.
Exactly, and they found that this more elaborate approach,
what they call generative, actually worked better.
So like forcing the model to explain itself
made it a better judge?
It seems that way, like if you can't explain it clearly,
you probably don't understand it well enough yourself.
Right, makes sense.
But all this training needs data, tons of it.
How did they make sure the reward model
was learning from the right kind of information?
They were very deliberate about the data they used.
They focused on creating a data set
with what they call outcome level supervision signals.
So basically a clear answer key for each problem.
Exactly, so the reward model knows for sure
when a solution is right or wrong.
Got it, no ambiguity there.
But did they just feed it a bunch of easy problems?
Or did they try to challenge it?
They actually used something called active learning.
Active learning.
Yeah, they strategically chose the most informative
and challenging examples for the reward model to learn from.
Oh, that's smart.
Like a teacher who knows which problems will really
make their students think.
Precisely.
And they also filtered out any redundant or biased examples
to prevent the reward model from developing bad habits.
So they're making sure it gets a balanced diet of math
problems, so to speak.
That's pretty cool.
But it makes me wonder, how much does the reward model's
performance really matter?
I mean, it's not the one actually solving the problems, right?
That's a great question.
And it turns out it matters a lot.
They did some tests to see how well the reward model could
judge individual reasoning steps.
Not just the final answer, but each step along the way.
Oh, wow.
So they were really putting it to the test.
They were.
And what they found was pretty impressive.
Even though the reward model was mainly
trained on whether the final answer was right or wrong,
it still got really good at assessing
the quality of those individual steps.
That's amazing.
It's like a music teacher who can not only tell
if you played the wrong note, but can also
critique your technique in phrasing.
Exactly.
It suggests that the reward model
has developed a deeper understanding of what
good reasoning looks like, even beyond just
getting the right answer.
That's really cool.
So it's not just about the outcome.
It's about the process, too.
Precisely.
And that opens up some really exciting possibilities
for the future of AI.
OK, so we've talked about this framework, this policy model,
this reward model, these search algorithms.
But where does all this leave us?
What's the big takeaway from this research?
I think the main message is that we're making real progress
towards building LLMs that can reason more like humans.
This paper gives us a solid framework for doing that,
and it shows us some of the key ingredients we need,
things like structured training data,
thoughtful reward modeling, and those strategic search
algorithms.
And while there's still a lot of work to be done,
the results are pretty encouraging.
Absolutely.
We might not have AI that can solve all our problems just
yet, but this research definitely brings us one step
closer to that goal.
That's for sure.
And it raises some really interesting questions
about the future.
Like, if we can create LLMs that are this good at reasoning,
what are the possibilities?
Yeah, what could this mean for fields like medicine,
engineering, or even art?
Those are definitely questions worth pondering.
And on that note, we'll wrap up today's deep dive.
Thanks for joining us on this journey
into the fascinating world of AI reasoning.
Until next time, keep those minds curious
and those algorithms humming.
It's all about how they train that policy model
to think step by step, like a human would.
Right.
Like we were talking about with those example solutions
from the bigger LLM.
Yeah, exactly.
Like those examples weren't just about the answer.
They were about showing the whole process.
Right.
So it wasn't just, here's the answer.
It was more like, here's how we got to the answer.
Precisely.
Think of it like having a math tutor who doesn't just
give you the answer, but shows you their work every step,
clearly laid out.
Ah, OK.
So it's like seeing the logic behind each step,
the reasoning process.
Exactly.
And to really drill that in, they trained the policy model
on this data set called NUMINAMATH.
It's full of problems solved in this detailed step by step
format.
So it's got lots of good examples to learn from to see
how those steps fit together.
Right.
And they also used something called instruction tuning.
They actually gave the model clear instructions
on how to approach the problems.
Instruction tuning.
So it's not just learning by example.
It's getting explicit instructions too.
Exactly.
It's like, OK, first analyze the question,
then rephrase it in your own words,
and then break down the solution into labeled steps.
So it's like a checklist, a set of guidelines
for effective problem solving.
Right.
And that helps the model learn the right approach,
the step by step thinking.
Makes sense.
But we've talked a lot about the policy model.
What about that reward model?
How did they make sure it was judging the solutions accurately?
Well, they experimented with different ways
to train it to see what worked best.
They tried simple scoring, like a thumbs up or thumbs down.
But they also tried more complex evaluations.
Oh.
So instead of just saying good or bad,
the reward model had to explain why it thought
a solution was good or bad.
Exactly.
And they found that this more elaborate approach,
where the model had to justify its judgments,
actually led to better performance.
Interesting.
So forcing the model to explain itself
actually made it a better judge.
It seems that way.
Like if you can explain it clearly,
you probably don't understand it that well yourself.
Right.
Makes sense.
But all this training needs data, right?
Yeah.
How did they make sure the reward models was
getting the right kind of information?
They were really careful about the data they used,
focusing on creating a data set with clear outcome
level supervision signals.
Outcome levels.
So basically clear answer key for each problem.
So the model knows for sure if a solution is right or wrong.
Exactly.
No ambiguity there.
The reward model knows exactly when
it's looking at a correct solution.
Got it.
So it's got a solid foundation to learn from.
But did they just give it easy problems?
No.
They actually used a technique called active learning
to strategically pick the most informative and challenging
examples for the reward model.
So they were making sure it got a good mix,
not just the easy stuff.
Right.
And they also made sure to filter out
any redundant or biased examples.
So they were really being careful about the data,
making sure it was balanced and helpful.
Exactly.
They wanted to prevent the reward model
from developing bad habits or taking shortcuts.
Smart.
So they've put a lot of work into this reward model.
But how much does its performance really matter?
I mean, it's not directly solving the problems, right?
That's a great question.
And it turns out it matters a lot.
They did some tests to see how well the reward model could
judge individual steps in the reasoning process.
Oh, wow.
So not just the final answer, but each step along the way.
Exactly.
And what they found was pretty amazing.
Even though the reward model was trained on the final answer,
it could still assess the quality of those individual steps
really well.
That's really impressive.
So it's like it developed a deeper understanding
of good reasoning, not just getting the right answer.
Precisely.
And that's really exciting because it
means we could potentially build LLMs that can not only
solve problems, but also explain their thinking in a way
that we can understand.
That would be incredible.
So after this deep dive, what's the main takeaway for our listeners?
Well, I think the biggest takeaway
is that we're getting closer to building LLMs that reason more
like humans.
This research gives us a framework, some key ingredients,
and some really promising results.
It's definitely encouraging to see this progress.
It seems like we're on the right track.
Absolutely.
We might not have AI that can solve all our problems just
yet, but we're definitely moving in the right direction.
That's for sure.
And it makes you wonder, what could this mean for the future?
If we can get LLMs to reason this well,
what are the possibilities?
Yeah, how could this impact feels like medicine, engineering,
maybe even art?
So many possibilities.
It's definitely exciting to think about.
And on that note, we'll wrap up this deep dive.
Thanks for joining us on this journey
into the world of AI reasoning.
Until next time, keep those minds curious
and those algorithms humming.