So Google's Research Just Exposed OpenAI's Secrets (OpenAI o1-Exposed)

Summary

The 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

00:00

🤖 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.

05:01

🔍 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.

10:03

🛠️ 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.

15:03

🏃‍♂️ 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.

Keywords

💡Large Language Models (LLMs)

Large Language Models (LLMs) refer to AI systems that are trained on vast amounts of text data and can generate human-like text, answer complex questions, and perform various language-related tasks. In the video's context, LLMs like GPT-4, Claude 3.5, and Sonic are highlighted for their powerful capabilities but also their increasing resource intensity as they become more sophisticated.

💡Resource Intensive

The term 'resource intensive' describes the high computational and energy requirements needed to scale up large language models. As models grow in size and complexity, they demand more compute power, leading to higher costs, increased energy consumption, and potentially greater latency, especially in real-time or edge environments.

💡Test Time Compute

Test time compute refers to the computational effort used by a model when generating outputs, as opposed to during its training phase. It is likened to a student taking an exam, where the training is the study phase and test time is the application of knowledge. The video emphasizes the importance of optimizing test time compute to improve model efficiency without increasing model size.

💡Model Scaling

Model scaling traditionally involves increasing the number of parameters in a model to make it larger and more capable. The video discusses how this approach, while effective, leads to significant costs and challenges in deployment, prompting the need for alternative strategies like optimizing test time compute.

💡Verifier Reward Models

Verifier Reward Models are a mechanism introduced in the video where a separate model evaluates the steps taken by the main language model when solving a problem. This process-based approach allows the main model to search through multiple outputs, using the verifier to find the best one, thereby improving accuracy without increasing model size.

💡Adaptive Response Updating

Adaptive Response Updating is a concept where the model revises its response multiple times based on what it learns during the process. It is compared to a game of 20 questions, where each answer influences the next question asked. This allows the model to dynamically adjust its responses, improving output quality without pre-training, and is a key strategy in optimizing test time compute.

💡Compute Optimal Scaling Strategy

Compute optimal scaling strategy is a method of dynamically allocating computational resources based on the difficulty of a task or prompt. This contrasts with fixed computation strategies and allows models to maintain high performance across various tasks without being scaled up to enormous sizes, as illustrated by the video's discussion on efficient AI deployment.

💡Math Benchmark

The Math Benchmark is a dataset used in the video to test the deep reasoning and problem-solving skills of language models. It consists of high school level math problems designed to challenge models' abilities to not only provide correct answers but also understand the steps required to reach those answers, making it a robust test for the research's methods.

💡Fine-tuning

Fine-tuning in the context of the video refers to the process of further training a model on a specific task to improve its performance. The researchers fine-tuned Palm 2 models for revision and verification tasks, enabling them to iteratively improve answers and check each step of a solution, respectively.

💡Process Reward Models (PRMs)

Process Reward Models (PRMs) are used to predict the correctness of each step in a model's reasoning process, providing automated hints and making the search for correct answers more efficient. The video explains how PRMs, combined with adaptive search methods, allow models to dynamically allocate computing power where it's most needed, achieving better results with less computation.

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