So Google's Research Just Exposed OpenAI's Secrets (OpenAI o1-Exposed)
Summary
TLDRThe video explores advancements in AI, particularly focusing on the shift from scaling large language models (LLMs) to optimizing test-time compute for better efficiency. It contrasts traditional methods of making models larger with new approaches, such as adaptive response updating and verifier reward models, that allow smaller models to think longer and smarter during inference. Research from Google DeepMind suggests these techniques can outperform much larger models while using fewer resources. This shift signals a more efficient future for AI, moving away from brute-force scaling towards smarter compute allocation.
Takeaways
- 🤖 Large Language Models (LLMs) like GPT-4, Claude 3.5, and others have become incredibly powerful, but are resource-intensive to scale.
- 💡 Scaling LLMs by adding more parameters increases their capabilities, but also significantly raises costs, energy consumption, and complexity in deployment.
- 🔄 Test time compute optimization offers a smarter alternative, focusing on how efficiently models use computational resources during inference rather than just making them larger.
- 📚 Test time compute is the computational effort used by a model when generating outputs, similar to a student taking an exam after studying.
- ⚡ Scaling models leads to diminishing returns as performance plateaus while costs continue to rise.
- 🔍 Verifier reward models help optimize test time compute by verifying reasoning steps, similar to a built-in quality checker.
- 🎯 Adaptive response updating allows models to refine their answers based on previous outputs, enhancing accuracy without increasing model size.
- 🛠 Compute-optimal scaling dynamically allocates computational resources based on task difficulty, ensuring efficiency in performance without massive scaling.
- 📊 Techniques like fine-tuning revision models and process reward models allow for better step-by-step reasoning and improved results using less computation.
- 🔬 DeepMind’s research, along with OpenAI’s, shows that smarter compute usage can lead to models that are as efficient as much larger models, marking a shift from the previous 'bigger is better' approach.
Q & A
What is the main challenge with scaling up large language models (LLMs)?
-Scaling up LLMs presents challenges such as increased resource intensity, higher costs, more energy consumption, and greater latency, especially for real-time or edge environment deployments.
Why is optimizing test time compute significant for AI deployment?
-Optimizing test time compute allows for smaller models to think longer or more effectively during inference, potentially revolutionizing AI deployment in resource-limited settings without compromising performance.
What is test time compute and why is it important?
-Test time compute refers to the computational effort used by a model when generating outputs, as opposed to during its training phase. It's important because it impacts the efficiency and cost of deploying AI models in real-world applications.
How does scaling model parameters affect the performance and cost of AI models?
-Scaling model parameters by making models larger can significantly increase performance but also leads to higher costs due to increased compute power requirements for both training and inference.
What are the two main mechanisms introduced by DeepMind for optimizing test time compute?
-The two main mechanisms are verifier reward models, which evaluate and refine the model's outputs, and adaptive response updating, which allows the model to dynamically adjust its responses based on learned information.
How does the verifier reward model work in the context of AI?
-A verifier reward model is a separate model that evaluates the steps taken by the main language model when solving a problem, helping it to search through multiple possible outputs and choose the best one.
What is adaptive response updating and how does it improve model performance?
-Adaptive response updating allows the model to revise its answers multiple times, taking into account its previous attempts to improve its output without needing extra pre-training.
What is compute optimal scaling and how does it differ from fixed computation strategies?
-Compute optimal scaling is a strategy that dynamically allocates compute resources based on the difficulty of the task. It differs from fixed computation strategies by adapting compute power to the task's needs, making it more efficient.
What is the Math Benchmark and why was it chosen for testing the new techniques?
-The Math Benchmark is a collection of high school level math problems designed to test deep reasoning and problem-solving skills. It was chosen because it challenges the model's ability to refine answers and verify steps, which are the core goals of the research.
How does fine-tuning revision models help in optimizing test time compute?
-Fine-tuning revision models teaches the model to iteratively improve its own answers, similar to a student self-correcting mistakes, allowing for more accurate and refined outputs without increasing model size.
What are the potential benefits of using compute optimal scaling in real-world AI applications?
-Using compute optimal scaling can lead to more efficient AI models that perform at or above the level of much larger models by being strategic about computational power, resulting in lower costs and reduced energy consumption.
Outlines
🤖 Challenges in Scaling Large Language Models
The paragraph discusses the evolution and challenges of large language models (LLMs) like GPT, Claude, and Sonic. These models have become powerful tools for various applications but face issues with scaling due to increased resource intensity. As models grow in complexity, they demand more compute power, leading to higher costs, energy consumption, and latency, especially in real-time or edge environments. The need for optimization at test time compute is introduced as an alternative to simply increasing model size.
🔍 Test Time Compute vs. Model Scaling
This section delves into the concept of test time compute, which is the computational effort used by a model during output generation rather than during training. It contrasts the traditional approach of scaling model parameters by increasing size with the idea of optimizing test time compute for efficiency. The paragraph highlights the downsides of scaling up models, such as high costs, energy consumption, and deployment challenges, and suggests that optimizing test time compute could offer a more strategic alternative.
🛠️ Innovative Approaches to Test Time Compute
The paragraph introduces two mechanisms developed by Google DeepMind to optimize test time compute without scaling up the model itself: verifier reward models and adaptive response updating. Verifier reward models involve a separate model that evaluates the main language model's steps, improving accuracy by ensuring sound reasoning at each step. Adaptive response updating allows the model to refine its answers dynamically based on learned information, akin to playing a game of 20 questions. These approaches aim to make models smarter and more efficient.
🏃♂️ Compute Optimal Scaling Strategy
This section explains the compute optimal scaling strategy, which dynamically allocates computational resources based on the difficulty of the task, much like pacing oneself in a marathon. It contrasts this with fixed computation strategies that use the same amount of compute power for every task. The strategy is shown to be more efficient, as it allows models to maintain high performance across various tasks without being excessively large. The effectiveness of these techniques is tested using the math benchmark, a dataset designed to challenge deep reasoning and problem-solving skills.
📊 Performance Results and Future Implications
The final paragraph discusses the results of implementing the compute optimal scaling strategy, which show that models can achieve similar or better performance with significantly less computation compared to traditional methods. It draws parallels with OpenAI's AO1 model, emphasizing the shift towards smarter compute usage in AI models. The paragraph concludes by suggesting that the future of AI will be explosive as the industry moves towards more efficient models that perform at or above the level of much larger ones by being strategic about computational power.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Resource Intensive
💡Test Time Compute
💡Model Scaling
💡Verifier Reward Models
💡Adaptive Response Updating
💡Compute Optimal Scaling Strategy
💡Math Benchmark
💡Fine-tuning
💡Process Reward Models (PRMs)
Highlights
New research from Google DeepMind challenges the conventional scaling of large language models (LLMs).
LLMs like GPT-4, Claude 3.5, and Sonic have become powerful but are increasingly resource-intensive.
Scaling up model parameters requires significant compute power, leading to higher costs and energy consumption.
The need for optimization of test-time compute is emphasized for practical AI deployment with limited resources.
Test-time compute refers to the computational effort during output generation, not during training.
Large language models are designed to be powerful immediately, necessitating large sizes.
Scaling models leads to downsides such as high costs, energy consumption, and deployment challenges.
Optimizing test-time compute could revolutionize AI deployment by making smaller models think more effectively.
The 'bigger is better' approach to models has significant costs and diminishing returns.
Optimizing test-time compute offers a strategic alternative to relying on massive models.
Verifier reward models allow a separate model to evaluate and improve the main language model's reasoning steps.
Adaptive response updating lets the model refine its answers based on what it learns, similar to playing a game of 20 questions.
Compute optimal scaling strategy dynamically allocates compute resources based on task difficulty.
The Math Benchmark, a collection of high school level math problems, is used to test model performance.
Palm 2, a Google language model, is fine-tuned for revision and verification tasks in this research.
Fine-tuning revision models teaches the model to iteratively improve its own answers.
Process reward models (PRMs) and adaptive search methods help the model find the best possible answers efficiently.
Compute optimal scaling adapts computation based on task difficulty, using less computation for similar performance.
Smaller models using compute optimal scaling can outperform much larger models, indicating a shift towards efficiency.
The future of AI is poised for explosive growth with smarter, more efficient models that perform at or above larger ones.
Transcripts
do you remember the new open ao1 model
where the model thinks before it
responds and is now at the level of a
PhD well there's new research from
Google deep mind that somewhat breaks
down this method and shows that the ways
we were scaling llms before might not
have been the most optimal before we
dive into the details let's take a step
back and understand the landscape of
large language models over the past few
years llms like GPT 4 Claude 3.5 Sonic
and others have become incredibly
powerful tools capable of generating
humanlike text answering complex
questions coding tutoring and even
engaging in philosophical debates their
widespread applications have set new
benchmarks for AI capabilities however
there's a catch as these models become
more sophisticated they also become more
resource intensive scaling up model
parameters which is essentially making
them larger and more complex requires
enormous amounts of compute power that
means higher costs more energy
consumption and greater latency
especially when you're deploying these
mod models in real time or Edge
environment and it's not just the
infrastructure pre-training these
massive models demands huge data sets
and months of training time given these
challenges it's clear that we need to
think Beyond just making these models
bigger this is where the idea of
optimizing test time compute comes in so
what we're going to take a look at is
instead of training a model to be a jack
of all trades by making it larger what
if we could make a smaller model think
longer or more effectively during
inference this could revolutionize how
we think about deploying AI in Practical
settings where resources are limited but
performance still matters test time
compute versus model scaling to
understand this we first need to Define
what we mean by test time compute test
time compute refers to the computational
effort used by a model when it's
generating outputs rather than during
its training phase think of it as the
difference between a student studying
for an exam and actually taking it
training is like the study phase where
all the learning happens while test time
computation is like the exam phase where
that knowledge is put to use to answer
questions or solve problems so so why is
test time compute important well as it
stands most large language models like
GPT 40 or Claude 3.5 Sonet are designed
to be incredibly powerful right out of
the gate which means they need to be big
really big but here's the catch scaling
these models to massive sizes has some
pretty serious downsides first there's
the cost more parameters mean more
compute power which translates to higher
costs for both training and inference
and it's not just about the money it's
also about energy consumption running
these models requires vast amounts of
electricity which isn't exactly great
for the environment then there's the
deployment challenge huge models are
difficult to deploy especially in
settings where computational resources
are limited like on mobile devices or
Edge servers given these challenges the
question becomes can we get the same or
even better performance without scaling
up the model itself that's where
optimizing test time compute comes in by
allocating computational resources more
efficiently during inference we can
potentially boost a model's performance
without needing to make it bigger the
dominant strategy over the past few
years has been relatively
straightforward just make the models
bigger this involves increasing the
number of parameters in a model which
essentially means adding more layers
more neurons and more connections
between them this method has proven
effective no doubt it's why gpt3 with
175 billion parameters was significantly
more powerful than gpt2 with only 1.5
billion and it's why even larger models
like GPT 4 or o continue to push the
boundaries of what's possible with
natural language processing more
parameters generally mean a more capable
model that can understand more context
generate more coherent and nuanced
responses and even perform better on a
range of tasks however this bigger is
better approach comes with significant
costs training a model with hundreds of
billions of parameters requires massive
data sets sophisticated infrastructure
and months of compute time on thousands
of gpus not to mention the inference the
actual usage of these models in real
world applications also becomes
computationally expensive every time you
ask the model a question or prompt it to
generate text it requires a lot of
compute power which adds up quickly in
production environments this is why
companies like open Ai and Google are
looking for smarter ways to achieve high
performance without just throwing more
compute and data at the problem now
let's consider the trade-offs between
these two approaches scaling model
parameters versus optimizing test time
compute on one hand scaling model
parameters is a Brute Force approach it
works but it's costly inefficient and
has diminishing returns as models get
larger imagine a graph showing compute
cost on one axis and performance on the
other as you increase model size the
performance gains start to Plateau while
the costs continue to soar upward not a
great return on investment on the other
hand optimizing test time compute offers
a more strategic alternative instead of
relying on massive models we could
deploy smaller more efficient models
that you additional computation
selectively during inference to improve
their outputs think of it like a
sprinter conserving energy until the
final stretch and then giving it their
all when it matters most however this
approach isn't without its own
challenges for example designing
effective strategies to allocate compute
during test time is a non-trivial task
you need to decide when and how much
extra compute to use based on the
complexity of the problem at hand but
the potential upside is significant you
could achieve comparable performance to
a much larger model using less computer
lower costs and reduced energy
consumption what does this all mean in
practice the key takeaway here is that
there's a balance to be struck in some
cases adding more parameters might still
be the best approach particularly for
extremely complex tasks where Brute
Force scale is necessary but in many
other cases especially For Less complex
tasks or when deploying models in
resource constrained environments
optimizing test time compute could be a
GameChanger and that's exactly what this
deepmind research is exploring how to
find that optimal balance and what
techniques can help us get the most out
of every compute cycle now that we've
set the stage by understanding the
problem of test time compute versus
model scaling let's move on to some of
the key Concepts introduced in this
paper the researchers have developed two
main mechanisms to scale up compute
during the models usage phase what we
call test time without needing to scale
up the model itself the first mechanism
is called verifier reward models now
that might sound a bit technical so
let's simplify it imagine you're taking
a multiple choice test and after
answering a question you have a friend
who is a genius in that subject check
your answer your friend doesn't just
tell you if the answer is right or wrong
they also help you figure out the steps
that led to the right answer you could
then use this feedback to improve your
next answer that's kind of what a
verifier reward model does for large
language model and so in technical terms
a verifier is a separate model that
evaluates or verifies the steps taken by
the main language model when it tries to
solve a problem instead of just
generating an output and moving on the
model searches through multiple possible
outputs or answers and uses the verifier
to find the best one the verifier acts
like a filter scoring each option based
on how good it is and then helping the
model choose the best path forward this
process-based approach meaning it
evaluates each step in the process not
just the final answer helps the model
become more accurate by ensuring that
every part of its reasoning is sound
it's like having a built-in quality
Checker that allows the model to revise
and improve its answers dynamically in
Practical terms this means a model
doesn't have to be massive to be smart
it just needs a good system to check its
work by incorporating verifier reward
models we can optimize how models use
their compute during test time making
them both faster and more accurate
without needing to be enormous the
second mechanism is known as adaptive
response updating think of this like
playing a game of 20 questions if you've
ever played you know that each question
you ask changes based on the answers you
get if you find out the answer is a
fruit you stop asking if it's an animal
similarly adaptive response updating is
about allowing the model to adapt and
refine its answers on the Fly based on
what it learns as it go here's how it
works when the model is asked a
challenging question or given a complex
task instead of just spitting out one
answer it revises its response multiple
times each time it does this it takes
into account what it got right and wrong
in the previous attempt this allows it
to zero in on the correct answer more
effectively in more technical terms this
means that the model dynamically adjusts
its response distribution at test time
think of response distribution like I
said of possible answers the model might
give by adapting this distribution based
on what it's learning in real time the
model can improve its output without
needing extra pre-training it's like
having the ability to think harder or
think smarter when the problem is tough
rather than just rushing to a conclusion
this approach is powerful because it
turns the model from a static responder
where it only gives you one answer into
a more Dynamic thinker capable of
adjusting its strategies based on the
problem it faces and again this can be
done without making the model itself
bigger fig which is a game Cher for
deploying these models in Practical real
world scenarios now let's bring these
two concepts together with what the
researchers call a compute optimal
scaling strategy don't worry it sounds
more complex than it is at its core
compute optimal scaling is about being
smart with how we use computing power
instead of using a fixed amount of
compute for every single problem this
strategy allocates compute resources
dynamically based on the difficulty of
the task or prompt so for example
imagine you're running a marathon you
wouldn't Sprint the entire way you'd
pace yourself you'd run faster in some
sections and slow down in others based
on the terrain similarly the compute
optimal strategy does something like
this for models if the model is given an
easy problem it might not use much
compute at all it can just Breeze
through it but if the problem is tough
the model will allocate more compute
like running faster in a marathon to
think more deeply use verifier models or
make adaptive updates to find the best
answer now how is this different from
fixed computation strategies which is
what most models use today well most
traditional models use the same amount
of compute power for every task no
matter how easy or hard it's like
running at the same speed for an entire
Marathon whether you're going uphill or
downhill pretty inefficient right
compute optimal scaling on the other
hand adjust based on need making it much
more efficient by using compute
adaptively models can maintain high
performance across a variety of tasks
without needing to be scaled up to
gigantic sizes to truly understand the
effectiveness of these new techniques
for scaling test time compute deep Minds
researchers had to put them to the test
using real world data and for this they
chose a particularly challenging data
set known as the math benchmark so what
is the math benchmark imagine a
collection of high school level math
problems everything from algebra and
geometry to calculus and combinatoric
these aren't your standard math problems
either they're specifically designed to
test deep reasoning and problem solving
skills which makes them a perfect
challenge for large language models the
idea is to see if a model can not only
come up with with the right answer but
also understand the steps needed to get
there this makes the math benchmark
ideal for experiments focusing on
refining answers and verifying steps
which are the core goals of This
research by using this data set the
researchers could rigorously evaluate
how well the proposed methods perform
across a range of difficulty levels from
relatively straightforward problems to
those that require complex multi-step
reasoning the choice of this Benchmark
ensures that the findings are robust and
applicable to real world tasks that
demand strong logical and analytical
skills next let's talk about the models
themselves for This research the team
used Palm 2 models specifically
fine-tuned versions known as palm 2 now
Palm 2 or Pathways language model is one
of Google's Cutting Edge language models
known for its powerful natural language
processing capabilities it's a great
choice for this study because it already
has a strong foundation in understanding
and generating complex text which is
crucial for solving math problems and
verifying reasoning however for This
research they didn't just use the
off-the-shelf version of palm 2 they
took things things a step further by
fine-tuning these models specifically
for two key tasks revision and
verification revision tasks this
involves training the model to
iteratively improve its own answers
think of it like a student going through
their homework and correcting Mistakes
One Step at a Time verification task
this is about checking each step in a
solution to make sure it's accurate much
like a teacher reviewing a student's
work to provide feedback on every part
of the process by fine-tuning Palm 2 in
these specific ways the researchers
created specialized versions of the
model that are highly skilled at
refining responses and verifying
Solutions which are crucial abilities
for optimizing test time compute now
that we've covered the models and data
sets let's dig into the core techniques
and approaches that were tested in this
research the research has focused on
three main areas fine-tuning revision
models training process reward models
prms for search methods and first up we
have fine-tuning revision models the
goal here was to teach the model how to
revise its own answers iteratively think
of it like teaching a student to
self-correct their mistakes but here's
the Big Catch the model isn't just
correcting a single mistake and stopping
it's trained to go back and keep
improving its answer step by step until
it gets it right so how did they do this
the researchers used a process called
supervised fine-tuning they created data
sets of multi- turn rollouts where the
model starts with an incorrect answer
and iteratively improves it until it
gets to the correct one but there were
some challenges for one generating high
quality training data for this kind of
task is tough because the model needs to
understand the context of previous
answers to make better revision to
handle this the re Searchers sampled
multiple possible answers and then
constructed training sequences that
combined Incorrect and correct answers
this way the model learns not just to
retry but to revise intelligently using
the context of what it got wrong
previously and the result a model that
doesn't just spit out a single answer
but can think through and refine its
responses like a careful student
tackling a tough math problem next we
have process reward models prms and
adaptive search methods prms help the
model verify each step of its reasoning
process by predicting how correct each
step is based on PR previous data
without needing human input this is like
solving a puzzle where the model gets
automated hints on whether it's on the
right path making the search for the
correct answer more efficient and
accurate instead of waiting until the
end to see if it's right or wrong the
model can adjust its steps in real time
similar to having a guide that helps
navigate each turn the research also
explores various search methods like
best of n beam search and look ahead
search which help the model find the
best possible answers by trying
different parts best of n is like taking
multiple shots and picking the best one
beams Search keeps multiple options open
and prunes the less promising ones as it
goes and look ahead search looks several
steps ahead to avoid dead Ends by
combining these search methods with prms
the model can dynamically allocate
computing power where it's needed most
achieving better results with less
computation and potentially
outperforming much larger models this
approach allows for smarter more
efficient AI that can handle complex
tasks without requiring enormous
computational resources so taking a look
at everything we can see that this
strategy called compute optimal scaling
adapts the amount of computation based
on the difficulty of a task the results
show that using this method models can
achieve similar or even better
performance while using four times less
computation compared to traditional
methods in some cases a smaller model
using this strategy can even outperform
a model that is 14 times larger this
approach is somewhat similar to open
ai's recent 01 model release which also
focuses on smarter compute usage open
ai's 01 model ranks in the 89th
percentile on competitive programming
problems places among the top 500 in the
US on a high level math competition and
exceeds human PhD level accuracy on
scientific questions 01 improves with
more compute both during training and at
test time so where we look at things
aheading both open Ai and Deep Mind
demonstrate that by optimizing how and
where computation is used whether during
learning or when generating answers AI
models can achieve high performance
without needing to be excessively large
this allows for more efficient models
that perform at or above the level of
much bigger ones by being strategic
about their computational power so
previously the Paradigm where
individuals thought that scale is all
you need the vibe seems to be shifting
away from this as we look to more
efficient ways to get smarter models and
I think that looking into the future
this shows us that the future of AI is
going to be an explosive one
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